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Investigating YOLO Models Towards Outdoor Obstacle Detection For Visually Impaired People (2312.07571v1)

Published 10 Dec 2023 in cs.CV, cs.AI, and cs.LG

Abstract: The utilization of deep learning-based object detection is an effective approach to assist visually impaired individuals in avoiding obstacles. In this paper, we implemented seven different YOLO object detection models \textit{viz}., YOLO-NAS (small, medium, large), YOLOv8, YOLOv7, YOLOv6, and YOLOv5 and performed comprehensive evaluation with carefully tuned hyperparameters, to analyze how these models performed on images containing common daily-life objects presented on roads and sidewalks. After a systematic investigation, YOLOv8 was found to be the best model, which reached a precision of $80\%$ and a recall of $68.2\%$ on a well-known Obstacle Dataset which includes images from VOC dataset, COCO dataset, and TT100K dataset along with images collected by the researchers in the field. Despite being the latest model and demonstrating better performance in many other applications, YOLO-NAS was found to be suboptimal for the obstacle detection task.

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References (37)
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Computer Vision: A Reference Guide, 1–9 (2020) Contributors [2022] Contributors, M.: MMYOLO: OpenMMLab YOLO series toolbox and benchmark. https://github.com/open-mmlab/mmyolo (2022) Jocher et al. [2022] Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., Kwon, Y., Michael, K., Fang, J., Yifu, Z., Wong, C., Montes, D., et al.: ultralytics/yolov5: v7. 0-yolov5 sota realtime instance segmentation. Zenodo (2022) Jocher [2022] Jocher, G.: YOLOv5 v6.1 Release. https://community.ultralytics.com/t/yolov5-v6-1-release/51 (2022) Li et al. [2022] Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., Nie, W., Li, Y., Zhang, B., Liang, Y., Zhou, L., Xu, X., Chu, X., Wei, X., Wei, X.: YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications (2022) Li et al. [2023] Li, C., Li, L., Geng, Y., Jiang, H., Cheng, M., Zhang, B., Ke, Z., Xu, X., Chu, X.: YOLOv6 v3.0: A Full-Scale Reloading (2023) Wang et al. [2022] Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors (2022) Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics [32] What is YOLOv8? https://roboflow.com/model/yolov8 (2023) Aharon et al. [2021] Aharon, S., Louis-Dupont, Ofri Masad, Yurkova, K., Lotem Fridman, Lkdci, Khvedchenya, E., Rubin, R., Bagrov, N., Tymchenko, B., Keren, T., Zhilko, A., Eran-Deci: Super-Gradients. GitHub (2021). https://doi.org/10.5281/ZENODO.7789328 . https://zenodo.org/record/7789328 [34] YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Frick, K.D., Joy, S.M., Wilson, D.A., Naidoo, K.S., Holden, B.A.: The global burden of potential productivity loss from uncorrected presbyopia. Ophthalmology 122(8), 1706–1710 (2015) Evans et al. [2007] Evans, J.R., Fletcher, A.E., Wormald, R.P.: Depression and anxiety in visually impaired older people. Ophthalmology 114(2), 283–288 (2007) Brunes et al. [2018] Brunes, A., Nielsen, M.B., Heir, T.: Bullying among people with visual impairment: prevalence, associated factors and relationship to self-efficacy and life satisfaction. World journal of psychiatry 8(1), 43 (2018) Eckert et al. [2015] Eckert, K.A., Carter, M.J., Lansingh, V.C., Wilson, D.A., Furtado, J.M., Frick, K.D., Resnikoff, S.: A simple method for estimating the economic cost of productivity loss due to blindness and moderate to severe visual impairment. Ophthalmic epidemiology 22(5), 349–355 (2015) Tapu et al. [2014] Tapu, R., Mocanu, B., Zaharia, T.: Real time static/dynamic obstacle detection for visually impaired persons. In: 2014 IEEE International Conference on Consumer Electronics (ICCE), pp. 394–395 (2014). IEEE Elsken et al. [2019] Elsken, T., Metzen, J.H., Hutter, F.: Neural architecture search: A survey. The Journal of Machine Learning Research 20(1), 1997–2017 (2019) Said et al. [2023] Said, Y., Atri, M., Albahar, M.A., Ben Atitallah, A., Alsariera, Y.A.: Obstacle detection system for navigation assistance of visually impaired people based on deep learning techniques. Sensors 23(11), 5262 (2023) Rachburee and Punlumjeak [2021] Rachburee, N., Punlumjeak, W.: An assistive model of obstacle detection based on deep learning: Yolov3 for visually impaired people. International Journal of Electrical & Computer Engineering (2088-8708) 11(4) (2021) Yadav et al. [2020] Yadav, S., Joshi, R.C., Dutta, M.K., Kiac, M., Sikora, P.: Fusion of object recognition and obstacle detection approach for assisting visually challenged person. In: 2020 43rd International Conference on Telecommunications and Signal Processing (TSP), pp. 537–540 (2020). IEEE Redmon et al. [2016] Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016) Redmon and Farhadi [2018] Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018) Liu et al. [2016] Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21–37 (2016). Springer Girshick et al. [2014] Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) Girshick [2015] Girshick, R.: Fast R-CNN (2015) Ren et al. [2016] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. Computer Vision: A Reference Guide, 1–9 (2020) Contributors [2022] Contributors, M.: MMYOLO: OpenMMLab YOLO series toolbox and benchmark. https://github.com/open-mmlab/mmyolo (2022) Jocher et al. [2022] Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., Kwon, Y., Michael, K., Fang, J., Yifu, Z., Wong, C., Montes, D., et al.: ultralytics/yolov5: v7. 0-yolov5 sota realtime instance segmentation. Zenodo (2022) Jocher [2022] Jocher, G.: YOLOv5 v6.1 Release. https://community.ultralytics.com/t/yolov5-v6-1-release/51 (2022) Li et al. [2022] Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., Nie, W., Li, Y., Zhang, B., Liang, Y., Zhou, L., Xu, X., Chu, X., Wei, X., Wei, X.: YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications (2022) Li et al. [2023] Li, C., Li, L., Geng, Y., Jiang, H., Cheng, M., Zhang, B., Ke, Z., Xu, X., Chu, X.: YOLOv6 v3.0: A Full-Scale Reloading (2023) Wang et al. [2022] Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors (2022) Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics [32] What is YOLOv8? https://roboflow.com/model/yolov8 (2023) Aharon et al. [2021] Aharon, S., Louis-Dupont, Ofri Masad, Yurkova, K., Lotem Fridman, Lkdci, Khvedchenya, E., Rubin, R., Bagrov, N., Tymchenko, B., Keren, T., Zhilko, A., Eran-Deci: Super-Gradients. GitHub (2021). https://doi.org/10.5281/ZENODO.7789328 . https://zenodo.org/record/7789328 [34] YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Evans, J.R., Fletcher, A.E., Wormald, R.P.: Depression and anxiety in visually impaired older people. Ophthalmology 114(2), 283–288 (2007) Brunes et al. [2018] Brunes, A., Nielsen, M.B., Heir, T.: Bullying among people with visual impairment: prevalence, associated factors and relationship to self-efficacy and life satisfaction. World journal of psychiatry 8(1), 43 (2018) Eckert et al. [2015] Eckert, K.A., Carter, M.J., Lansingh, V.C., Wilson, D.A., Furtado, J.M., Frick, K.D., Resnikoff, S.: A simple method for estimating the economic cost of productivity loss due to blindness and moderate to severe visual impairment. Ophthalmic epidemiology 22(5), 349–355 (2015) Tapu et al. [2014] Tapu, R., Mocanu, B., Zaharia, T.: Real time static/dynamic obstacle detection for visually impaired persons. In: 2014 IEEE International Conference on Consumer Electronics (ICCE), pp. 394–395 (2014). IEEE Elsken et al. [2019] Elsken, T., Metzen, J.H., Hutter, F.: Neural architecture search: A survey. The Journal of Machine Learning Research 20(1), 1997–2017 (2019) Said et al. [2023] Said, Y., Atri, M., Albahar, M.A., Ben Atitallah, A., Alsariera, Y.A.: Obstacle detection system for navigation assistance of visually impaired people based on deep learning techniques. Sensors 23(11), 5262 (2023) Rachburee and Punlumjeak [2021] Rachburee, N., Punlumjeak, W.: An assistive model of obstacle detection based on deep learning: Yolov3 for visually impaired people. International Journal of Electrical & Computer Engineering (2088-8708) 11(4) (2021) Yadav et al. [2020] Yadav, S., Joshi, R.C., Dutta, M.K., Kiac, M., Sikora, P.: Fusion of object recognition and obstacle detection approach for assisting visually challenged person. In: 2020 43rd International Conference on Telecommunications and Signal Processing (TSP), pp. 537–540 (2020). IEEE Redmon et al. [2016] Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016) Redmon and Farhadi [2018] Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018) Liu et al. [2016] Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21–37 (2016). Springer Girshick et al. [2014] Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) Girshick [2015] Girshick, R.: Fast R-CNN (2015) Ren et al. [2016] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. Computer Vision: A Reference Guide, 1–9 (2020) Contributors [2022] Contributors, M.: MMYOLO: OpenMMLab YOLO series toolbox and benchmark. https://github.com/open-mmlab/mmyolo (2022) Jocher et al. [2022] Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., Kwon, Y., Michael, K., Fang, J., Yifu, Z., Wong, C., Montes, D., et al.: ultralytics/yolov5: v7. 0-yolov5 sota realtime instance segmentation. Zenodo (2022) Jocher [2022] Jocher, G.: YOLOv5 v6.1 Release. https://community.ultralytics.com/t/yolov5-v6-1-release/51 (2022) Li et al. [2022] Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., Nie, W., Li, Y., Zhang, B., Liang, Y., Zhou, L., Xu, X., Chu, X., Wei, X., Wei, X.: YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications (2022) Li et al. [2023] Li, C., Li, L., Geng, Y., Jiang, H., Cheng, M., Zhang, B., Ke, Z., Xu, X., Chu, X.: YOLOv6 v3.0: A Full-Scale Reloading (2023) Wang et al. [2022] Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors (2022) Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics [32] What is YOLOv8? https://roboflow.com/model/yolov8 (2023) Aharon et al. [2021] Aharon, S., Louis-Dupont, Ofri Masad, Yurkova, K., Lotem Fridman, Lkdci, Khvedchenya, E., Rubin, R., Bagrov, N., Tymchenko, B., Keren, T., Zhilko, A., Eran-Deci: Super-Gradients. GitHub (2021). https://doi.org/10.5281/ZENODO.7789328 . https://zenodo.org/record/7789328 [34] YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Brunes, A., Nielsen, M.B., Heir, T.: Bullying among people with visual impairment: prevalence, associated factors and relationship to self-efficacy and life satisfaction. World journal of psychiatry 8(1), 43 (2018) Eckert et al. [2015] Eckert, K.A., Carter, M.J., Lansingh, V.C., Wilson, D.A., Furtado, J.M., Frick, K.D., Resnikoff, S.: A simple method for estimating the economic cost of productivity loss due to blindness and moderate to severe visual impairment. Ophthalmic epidemiology 22(5), 349–355 (2015) Tapu et al. [2014] Tapu, R., Mocanu, B., Zaharia, T.: Real time static/dynamic obstacle detection for visually impaired persons. In: 2014 IEEE International Conference on Consumer Electronics (ICCE), pp. 394–395 (2014). IEEE Elsken et al. [2019] Elsken, T., Metzen, J.H., Hutter, F.: Neural architecture search: A survey. The Journal of Machine Learning Research 20(1), 1997–2017 (2019) Said et al. [2023] Said, Y., Atri, M., Albahar, M.A., Ben Atitallah, A., Alsariera, Y.A.: Obstacle detection system for navigation assistance of visually impaired people based on deep learning techniques. Sensors 23(11), 5262 (2023) Rachburee and Punlumjeak [2021] Rachburee, N., Punlumjeak, W.: An assistive model of obstacle detection based on deep learning: Yolov3 for visually impaired people. International Journal of Electrical & Computer Engineering (2088-8708) 11(4) (2021) Yadav et al. [2020] Yadav, S., Joshi, R.C., Dutta, M.K., Kiac, M., Sikora, P.: Fusion of object recognition and obstacle detection approach for assisting visually challenged person. In: 2020 43rd International Conference on Telecommunications and Signal Processing (TSP), pp. 537–540 (2020). IEEE Redmon et al. [2016] Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016) Redmon and Farhadi [2018] Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018) Liu et al. [2016] Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21–37 (2016). Springer Girshick et al. [2014] Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) Girshick [2015] Girshick, R.: Fast R-CNN (2015) Ren et al. [2016] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. Computer Vision: A Reference Guide, 1–9 (2020) Contributors [2022] Contributors, M.: MMYOLO: OpenMMLab YOLO series toolbox and benchmark. https://github.com/open-mmlab/mmyolo (2022) Jocher et al. 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[2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics [32] What is YOLOv8? https://roboflow.com/model/yolov8 (2023) Aharon et al. [2021] Aharon, S., Louis-Dupont, Ofri Masad, Yurkova, K., Lotem Fridman, Lkdci, Khvedchenya, E., Rubin, R., Bagrov, N., Tymchenko, B., Keren, T., Zhilko, A., Eran-Deci: Super-Gradients. GitHub (2021). https://doi.org/10.5281/ZENODO.7789328 . https://zenodo.org/record/7789328 [34] YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. 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[2023] Said, Y., Atri, M., Albahar, M.A., Ben Atitallah, A., Alsariera, Y.A.: Obstacle detection system for navigation assistance of visually impaired people based on deep learning techniques. Sensors 23(11), 5262 (2023) Rachburee and Punlumjeak [2021] Rachburee, N., Punlumjeak, W.: An assistive model of obstacle detection based on deep learning: Yolov3 for visually impaired people. International Journal of Electrical & Computer Engineering (2088-8708) 11(4) (2021) Yadav et al. [2020] Yadav, S., Joshi, R.C., Dutta, M.K., Kiac, M., Sikora, P.: Fusion of object recognition and obstacle detection approach for assisting visually challenged person. In: 2020 43rd International Conference on Telecommunications and Signal Processing (TSP), pp. 537–540 (2020). IEEE Redmon et al. [2016] Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016) Redmon and Farhadi [2018] Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018) Liu et al. [2016] Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21–37 (2016). Springer Girshick et al. [2014] Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) Girshick [2015] Girshick, R.: Fast R-CNN (2015) Ren et al. [2016] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. Computer Vision: A Reference Guide, 1–9 (2020) Contributors [2022] Contributors, M.: MMYOLO: OpenMMLab YOLO series toolbox and benchmark. https://github.com/open-mmlab/mmyolo (2022) Jocher et al. [2022] Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., Kwon, Y., Michael, K., Fang, J., Yifu, Z., Wong, C., Montes, D., et al.: ultralytics/yolov5: v7. 0-yolov5 sota realtime instance segmentation. Zenodo (2022) Jocher [2022] Jocher, G.: YOLOv5 v6.1 Release. https://community.ultralytics.com/t/yolov5-v6-1-release/51 (2022) Li et al. [2022] Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., Nie, W., Li, Y., Zhang, B., Liang, Y., Zhou, L., Xu, X., Chu, X., Wei, X., Wei, X.: YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications (2022) Li et al. [2023] Li, C., Li, L., Geng, Y., Jiang, H., Cheng, M., Zhang, B., Ke, Z., Xu, X., Chu, X.: YOLOv6 v3.0: A Full-Scale Reloading (2023) Wang et al. [2022] Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors (2022) Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics [32] What is YOLOv8? https://roboflow.com/model/yolov8 (2023) Aharon et al. [2021] Aharon, S., Louis-Dupont, Ofri Masad, Yurkova, K., Lotem Fridman, Lkdci, Khvedchenya, E., Rubin, R., Bagrov, N., Tymchenko, B., Keren, T., Zhilko, A., Eran-Deci: Super-Gradients. GitHub (2021). https://doi.org/10.5281/ZENODO.7789328 . https://zenodo.org/record/7789328 [34] YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Tapu, R., Mocanu, B., Zaharia, T.: Real time static/dynamic obstacle detection for visually impaired persons. In: 2014 IEEE International Conference on Consumer Electronics (ICCE), pp. 394–395 (2014). IEEE Elsken et al. [2019] Elsken, T., Metzen, J.H., Hutter, F.: Neural architecture search: A survey. The Journal of Machine Learning Research 20(1), 1997–2017 (2019) Said et al. [2023] Said, Y., Atri, M., Albahar, M.A., Ben Atitallah, A., Alsariera, Y.A.: Obstacle detection system for navigation assistance of visually impaired people based on deep learning techniques. Sensors 23(11), 5262 (2023) Rachburee and Punlumjeak [2021] Rachburee, N., Punlumjeak, W.: An assistive model of obstacle detection based on deep learning: Yolov3 for visually impaired people. International Journal of Electrical & Computer Engineering (2088-8708) 11(4) (2021) Yadav et al. [2020] Yadav, S., Joshi, R.C., Dutta, M.K., Kiac, M., Sikora, P.: Fusion of object recognition and obstacle detection approach for assisting visually challenged person. In: 2020 43rd International Conference on Telecommunications and Signal Processing (TSP), pp. 537–540 (2020). IEEE Redmon et al. [2016] Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016) Redmon and Farhadi [2018] Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018) Liu et al. [2016] Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21–37 (2016). Springer Girshick et al. [2014] Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) Girshick [2015] Girshick, R.: Fast R-CNN (2015) Ren et al. [2016] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. Computer Vision: A Reference Guide, 1–9 (2020) Contributors [2022] Contributors, M.: MMYOLO: OpenMMLab YOLO series toolbox and benchmark. https://github.com/open-mmlab/mmyolo (2022) Jocher et al. [2022] Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., Kwon, Y., Michael, K., Fang, J., Yifu, Z., Wong, C., Montes, D., et al.: ultralytics/yolov5: v7. 0-yolov5 sota realtime instance segmentation. Zenodo (2022) Jocher [2022] Jocher, G.: YOLOv5 v6.1 Release. https://community.ultralytics.com/t/yolov5-v6-1-release/51 (2022) Li et al. [2022] Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., Nie, W., Li, Y., Zhang, B., Liang, Y., Zhou, L., Xu, X., Chu, X., Wei, X., Wei, X.: YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications (2022) Li et al. [2023] Li, C., Li, L., Geng, Y., Jiang, H., Cheng, M., Zhang, B., Ke, Z., Xu, X., Chu, X.: YOLOv6 v3.0: A Full-Scale Reloading (2023) Wang et al. [2022] Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors (2022) Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics [32] What is YOLOv8? https://roboflow.com/model/yolov8 (2023) Aharon et al. [2021] Aharon, S., Louis-Dupont, Ofri Masad, Yurkova, K., Lotem Fridman, Lkdci, Khvedchenya, E., Rubin, R., Bagrov, N., Tymchenko, B., Keren, T., Zhilko, A., Eran-Deci: Super-Gradients. GitHub (2021). https://doi.org/10.5281/ZENODO.7789328 . https://zenodo.org/record/7789328 [34] YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Elsken, T., Metzen, J.H., Hutter, F.: Neural architecture search: A survey. The Journal of Machine Learning Research 20(1), 1997–2017 (2019) Said et al. [2023] Said, Y., Atri, M., Albahar, M.A., Ben Atitallah, A., Alsariera, Y.A.: Obstacle detection system for navigation assistance of visually impaired people based on deep learning techniques. Sensors 23(11), 5262 (2023) Rachburee and Punlumjeak [2021] Rachburee, N., Punlumjeak, W.: An assistive model of obstacle detection based on deep learning: Yolov3 for visually impaired people. International Journal of Electrical & Computer Engineering (2088-8708) 11(4) (2021) Yadav et al. [2020] Yadav, S., Joshi, R.C., Dutta, M.K., Kiac, M., Sikora, P.: Fusion of object recognition and obstacle detection approach for assisting visually challenged person. In: 2020 43rd International Conference on Telecommunications and Signal Processing (TSP), pp. 537–540 (2020). IEEE Redmon et al. [2016] Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016) Redmon and Farhadi [2018] Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018) Liu et al. [2016] Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21–37 (2016). Springer Girshick et al. [2014] Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) Girshick [2015] Girshick, R.: Fast R-CNN (2015) Ren et al. [2016] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. Computer Vision: A Reference Guide, 1–9 (2020) Contributors [2022] Contributors, M.: MMYOLO: OpenMMLab YOLO series toolbox and benchmark. https://github.com/open-mmlab/mmyolo (2022) Jocher et al. [2022] Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., Kwon, Y., Michael, K., Fang, J., Yifu, Z., Wong, C., Montes, D., et al.: ultralytics/yolov5: v7. 0-yolov5 sota realtime instance segmentation. Zenodo (2022) Jocher [2022] Jocher, G.: YOLOv5 v6.1 Release. https://community.ultralytics.com/t/yolov5-v6-1-release/51 (2022) Li et al. [2022] Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., Nie, W., Li, Y., Zhang, B., Liang, Y., Zhou, L., Xu, X., Chu, X., Wei, X., Wei, X.: YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications (2022) Li et al. [2023] Li, C., Li, L., Geng, Y., Jiang, H., Cheng, M., Zhang, B., Ke, Z., Xu, X., Chu, X.: YOLOv6 v3.0: A Full-Scale Reloading (2023) Wang et al. [2022] Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors (2022) Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics [32] What is YOLOv8? https://roboflow.com/model/yolov8 (2023) Aharon et al. [2021] Aharon, S., Louis-Dupont, Ofri Masad, Yurkova, K., Lotem Fridman, Lkdci, Khvedchenya, E., Rubin, R., Bagrov, N., Tymchenko, B., Keren, T., Zhilko, A., Eran-Deci: Super-Gradients. GitHub (2021). https://doi.org/10.5281/ZENODO.7789328 . https://zenodo.org/record/7789328 [34] YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Said, Y., Atri, M., Albahar, M.A., Ben Atitallah, A., Alsariera, Y.A.: Obstacle detection system for navigation assistance of visually impaired people based on deep learning techniques. 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[2016] Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21–37 (2016). Springer Girshick et al. [2014] Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) Girshick [2015] Girshick, R.: Fast R-CNN (2015) Ren et al. [2016] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. Computer Vision: A Reference Guide, 1–9 (2020) Contributors [2022] Contributors, M.: MMYOLO: OpenMMLab YOLO series toolbox and benchmark. https://github.com/open-mmlab/mmyolo (2022) Jocher et al. [2022] Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., Kwon, Y., Michael, K., Fang, J., Yifu, Z., Wong, C., Montes, D., et al.: ultralytics/yolov5: v7. 0-yolov5 sota realtime instance segmentation. Zenodo (2022) Jocher [2022] Jocher, G.: YOLOv5 v6.1 Release. https://community.ultralytics.com/t/yolov5-v6-1-release/51 (2022) Li et al. [2022] Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., Nie, W., Li, Y., Zhang, B., Liang, Y., Zhou, L., Xu, X., Chu, X., Wei, X., Wei, X.: YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications (2022) Li et al. [2023] Li, C., Li, L., Geng, Y., Jiang, H., Cheng, M., Zhang, B., Ke, Z., Xu, X., Chu, X.: YOLOv6 v3.0: A Full-Scale Reloading (2023) Wang et al. [2022] Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors (2022) Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics [32] What is YOLOv8? https://roboflow.com/model/yolov8 (2023) Aharon et al. [2021] Aharon, S., Louis-Dupont, Ofri Masad, Yurkova, K., Lotem Fridman, Lkdci, Khvedchenya, E., Rubin, R., Bagrov, N., Tymchenko, B., Keren, T., Zhilko, A., Eran-Deci: Super-Gradients. GitHub (2021). https://doi.org/10.5281/ZENODO.7789328 . https://zenodo.org/record/7789328 [34] YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Rachburee, N., Punlumjeak, W.: An assistive model of obstacle detection based on deep learning: Yolov3 for visually impaired people. International Journal of Electrical & Computer Engineering (2088-8708) 11(4) (2021) Yadav et al. [2020] Yadav, S., Joshi, R.C., Dutta, M.K., Kiac, M., Sikora, P.: Fusion of object recognition and obstacle detection approach for assisting visually challenged person. In: 2020 43rd International Conference on Telecommunications and Signal Processing (TSP), pp. 537–540 (2020). IEEE Redmon et al. [2016] Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016) Redmon and Farhadi [2018] Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018) Liu et al. [2016] Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21–37 (2016). Springer Girshick et al. [2014] Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) Girshick [2015] Girshick, R.: Fast R-CNN (2015) Ren et al. [2016] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. Computer Vision: A Reference Guide, 1–9 (2020) Contributors [2022] Contributors, M.: MMYOLO: OpenMMLab YOLO series toolbox and benchmark. https://github.com/open-mmlab/mmyolo (2022) Jocher et al. [2022] Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., Kwon, Y., Michael, K., Fang, J., Yifu, Z., Wong, C., Montes, D., et al.: ultralytics/yolov5: v7. 0-yolov5 sota realtime instance segmentation. Zenodo (2022) Jocher [2022] Jocher, G.: YOLOv5 v6.1 Release. https://community.ultralytics.com/t/yolov5-v6-1-release/51 (2022) Li et al. [2022] Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., Nie, W., Li, Y., Zhang, B., Liang, Y., Zhou, L., Xu, X., Chu, X., Wei, X., Wei, X.: YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications (2022) Li et al. [2023] Li, C., Li, L., Geng, Y., Jiang, H., Cheng, M., Zhang, B., Ke, Z., Xu, X., Chu, X.: YOLOv6 v3.0: A Full-Scale Reloading (2023) Wang et al. [2022] Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors (2022) Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics [32] What is YOLOv8? https://roboflow.com/model/yolov8 (2023) Aharon et al. [2021] Aharon, S., Louis-Dupont, Ofri Masad, Yurkova, K., Lotem Fridman, Lkdci, Khvedchenya, E., Rubin, R., Bagrov, N., Tymchenko, B., Keren, T., Zhilko, A., Eran-Deci: Super-Gradients. GitHub (2021). https://doi.org/10.5281/ZENODO.7789328 . https://zenodo.org/record/7789328 [34] YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Yadav, S., Joshi, R.C., Dutta, M.K., Kiac, M., Sikora, P.: Fusion of object recognition and obstacle detection approach for assisting visually challenged person. In: 2020 43rd International Conference on Telecommunications and Signal Processing (TSP), pp. 537–540 (2020). IEEE Redmon et al. [2016] Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016) Redmon and Farhadi [2018] Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018) Liu et al. [2016] Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21–37 (2016). Springer Girshick et al. [2014] Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) Girshick [2015] Girshick, R.: Fast R-CNN (2015) Ren et al. [2016] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. Computer Vision: A Reference Guide, 1–9 (2020) Contributors [2022] Contributors, M.: MMYOLO: OpenMMLab YOLO series toolbox and benchmark. https://github.com/open-mmlab/mmyolo (2022) Jocher et al. [2022] Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., Kwon, Y., Michael, K., Fang, J., Yifu, Z., Wong, C., Montes, D., et al.: ultralytics/yolov5: v7. 0-yolov5 sota realtime instance segmentation. Zenodo (2022) Jocher [2022] Jocher, G.: YOLOv5 v6.1 Release. https://community.ultralytics.com/t/yolov5-v6-1-release/51 (2022) Li et al. [2022] Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., Nie, W., Li, Y., Zhang, B., Liang, Y., Zhou, L., Xu, X., Chu, X., Wei, X., Wei, X.: YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications (2022) Li et al. [2023] Li, C., Li, L., Geng, Y., Jiang, H., Cheng, M., Zhang, B., Ke, Z., Xu, X., Chu, X.: YOLOv6 v3.0: A Full-Scale Reloading (2023) Wang et al. [2022] Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors (2022) Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics [32] What is YOLOv8? https://roboflow.com/model/yolov8 (2023) Aharon et al. [2021] Aharon, S., Louis-Dupont, Ofri Masad, Yurkova, K., Lotem Fridman, Lkdci, Khvedchenya, E., Rubin, R., Bagrov, N., Tymchenko, B., Keren, T., Zhilko, A., Eran-Deci: Super-Gradients. GitHub (2021). https://doi.org/10.5281/ZENODO.7789328 . https://zenodo.org/record/7789328 [34] YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016) Redmon and Farhadi [2018] Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018) Liu et al. [2016] Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21–37 (2016). Springer Girshick et al. [2014] Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) Girshick [2015] Girshick, R.: Fast R-CNN (2015) Ren et al. [2016] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. Computer Vision: A Reference Guide, 1–9 (2020) Contributors [2022] Contributors, M.: MMYOLO: OpenMMLab YOLO series toolbox and benchmark. https://github.com/open-mmlab/mmyolo (2022) Jocher et al. [2022] Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., Kwon, Y., Michael, K., Fang, J., Yifu, Z., Wong, C., Montes, D., et al.: ultralytics/yolov5: v7. 0-yolov5 sota realtime instance segmentation. Zenodo (2022) Jocher [2022] Jocher, G.: YOLOv5 v6.1 Release. https://community.ultralytics.com/t/yolov5-v6-1-release/51 (2022) Li et al. [2022] Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., Nie, W., Li, Y., Zhang, B., Liang, Y., Zhou, L., Xu, X., Chu, X., Wei, X., Wei, X.: YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications (2022) Li et al. [2023] Li, C., Li, L., Geng, Y., Jiang, H., Cheng, M., Zhang, B., Ke, Z., Xu, X., Chu, X.: YOLOv6 v3.0: A Full-Scale Reloading (2023) Wang et al. [2022] Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors (2022) Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics [32] What is YOLOv8? https://roboflow.com/model/yolov8 (2023) Aharon et al. [2021] Aharon, S., Louis-Dupont, Ofri Masad, Yurkova, K., Lotem Fridman, Lkdci, Khvedchenya, E., Rubin, R., Bagrov, N., Tymchenko, B., Keren, T., Zhilko, A., Eran-Deci: Super-Gradients. GitHub (2021). https://doi.org/10.5281/ZENODO.7789328 . https://zenodo.org/record/7789328 [34] YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018) Liu et al. [2016] Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. 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[2016] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. 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[2020] Yadav, S., Joshi, R.C., Dutta, M.K., Kiac, M., Sikora, P.: Fusion of object recognition and obstacle detection approach for assisting visually challenged person. In: 2020 43rd International Conference on Telecommunications and Signal Processing (TSP), pp. 537–540 (2020). IEEE Redmon et al. [2016] Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016) Redmon and Farhadi [2018] Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018) Liu et al. [2016] Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21–37 (2016). Springer Girshick et al. [2014] Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) Girshick [2015] Girshick, R.: Fast R-CNN (2015) Ren et al. [2016] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. Computer Vision: A Reference Guide, 1–9 (2020) Contributors [2022] Contributors, M.: MMYOLO: OpenMMLab YOLO series toolbox and benchmark. https://github.com/open-mmlab/mmyolo (2022) Jocher et al. [2022] Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., Kwon, Y., Michael, K., Fang, J., Yifu, Z., Wong, C., Montes, D., et al.: ultralytics/yolov5: v7. 0-yolov5 sota realtime instance segmentation. Zenodo (2022) Jocher [2022] Jocher, G.: YOLOv5 v6.1 Release. https://community.ultralytics.com/t/yolov5-v6-1-release/51 (2022) Li et al. [2022] Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., Nie, W., Li, Y., Zhang, B., Liang, Y., Zhou, L., Xu, X., Chu, X., Wei, X., Wei, X.: YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications (2022) Li et al. [2023] Li, C., Li, L., Geng, Y., Jiang, H., Cheng, M., Zhang, B., Ke, Z., Xu, X., Chu, X.: YOLOv6 v3.0: A Full-Scale Reloading (2023) Wang et al. [2022] Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors (2022) Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics [32] What is YOLOv8? https://roboflow.com/model/yolov8 (2023) Aharon et al. [2021] Aharon, S., Louis-Dupont, Ofri Masad, Yurkova, K., Lotem Fridman, Lkdci, Khvedchenya, E., Rubin, R., Bagrov, N., Tymchenko, B., Keren, T., Zhilko, A., Eran-Deci: Super-Gradients. GitHub (2021). https://doi.org/10.5281/ZENODO.7789328 . https://zenodo.org/record/7789328 [34] YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Brunes, A., Nielsen, M.B., Heir, T.: Bullying among people with visual impairment: prevalence, associated factors and relationship to self-efficacy and life satisfaction. World journal of psychiatry 8(1), 43 (2018) Eckert et al. [2015] Eckert, K.A., Carter, M.J., Lansingh, V.C., Wilson, D.A., Furtado, J.M., Frick, K.D., Resnikoff, S.: A simple method for estimating the economic cost of productivity loss due to blindness and moderate to severe visual impairment. Ophthalmic epidemiology 22(5), 349–355 (2015) Tapu et al. [2014] Tapu, R., Mocanu, B., Zaharia, T.: Real time static/dynamic obstacle detection for visually impaired persons. In: 2014 IEEE International Conference on Consumer Electronics (ICCE), pp. 394–395 (2014). IEEE Elsken et al. [2019] Elsken, T., Metzen, J.H., Hutter, F.: Neural architecture search: A survey. The Journal of Machine Learning Research 20(1), 1997–2017 (2019) Said et al. [2023] Said, Y., Atri, M., Albahar, M.A., Ben Atitallah, A., Alsariera, Y.A.: Obstacle detection system for navigation assistance of visually impaired people based on deep learning techniques. Sensors 23(11), 5262 (2023) Rachburee and Punlumjeak [2021] Rachburee, N., Punlumjeak, W.: An assistive model of obstacle detection based on deep learning: Yolov3 for visually impaired people. International Journal of Electrical & Computer Engineering (2088-8708) 11(4) (2021) Yadav et al. [2020] Yadav, S., Joshi, R.C., Dutta, M.K., Kiac, M., Sikora, P.: Fusion of object recognition and obstacle detection approach for assisting visually challenged person. In: 2020 43rd International Conference on Telecommunications and Signal Processing (TSP), pp. 537–540 (2020). IEEE Redmon et al. [2016] Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016) Redmon and Farhadi [2018] Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018) Liu et al. [2016] Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21–37 (2016). Springer Girshick et al. [2014] Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) Girshick [2015] Girshick, R.: Fast R-CNN (2015) Ren et al. [2016] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. Computer Vision: A Reference Guide, 1–9 (2020) Contributors [2022] Contributors, M.: MMYOLO: OpenMMLab YOLO series toolbox and benchmark. https://github.com/open-mmlab/mmyolo (2022) Jocher et al. [2022] Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., Kwon, Y., Michael, K., Fang, J., Yifu, Z., Wong, C., Montes, D., et al.: ultralytics/yolov5: v7. 0-yolov5 sota realtime instance segmentation. Zenodo (2022) Jocher [2022] Jocher, G.: YOLOv5 v6.1 Release. https://community.ultralytics.com/t/yolov5-v6-1-release/51 (2022) Li et al. [2022] Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., Nie, W., Li, Y., Zhang, B., Liang, Y., Zhou, L., Xu, X., Chu, X., Wei, X., Wei, X.: YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications (2022) Li et al. [2023] Li, C., Li, L., Geng, Y., Jiang, H., Cheng, M., Zhang, B., Ke, Z., Xu, X., Chu, X.: YOLOv6 v3.0: A Full-Scale Reloading (2023) Wang et al. [2022] Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors (2022) Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics [32] What is YOLOv8? https://roboflow.com/model/yolov8 (2023) Aharon et al. [2021] Aharon, S., Louis-Dupont, Ofri Masad, Yurkova, K., Lotem Fridman, Lkdci, Khvedchenya, E., Rubin, R., Bagrov, N., Tymchenko, B., Keren, T., Zhilko, A., Eran-Deci: Super-Gradients. GitHub (2021). https://doi.org/10.5281/ZENODO.7789328 . https://zenodo.org/record/7789328 [34] YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Eckert, K.A., Carter, M.J., Lansingh, V.C., Wilson, D.A., Furtado, J.M., Frick, K.D., Resnikoff, S.: A simple method for estimating the economic cost of productivity loss due to blindness and moderate to severe visual impairment. Ophthalmic epidemiology 22(5), 349–355 (2015) Tapu et al. [2014] Tapu, R., Mocanu, B., Zaharia, T.: Real time static/dynamic obstacle detection for visually impaired persons. In: 2014 IEEE International Conference on Consumer Electronics (ICCE), pp. 394–395 (2014). IEEE Elsken et al. [2019] Elsken, T., Metzen, J.H., Hutter, F.: Neural architecture search: A survey. The Journal of Machine Learning Research 20(1), 1997–2017 (2019) Said et al. [2023] Said, Y., Atri, M., Albahar, M.A., Ben Atitallah, A., Alsariera, Y.A.: Obstacle detection system for navigation assistance of visually impaired people based on deep learning techniques. Sensors 23(11), 5262 (2023) Rachburee and Punlumjeak [2021] Rachburee, N., Punlumjeak, W.: An assistive model of obstacle detection based on deep learning: Yolov3 for visually impaired people. International Journal of Electrical & Computer Engineering (2088-8708) 11(4) (2021) Yadav et al. [2020] Yadav, S., Joshi, R.C., Dutta, M.K., Kiac, M., Sikora, P.: Fusion of object recognition and obstacle detection approach for assisting visually challenged person. In: 2020 43rd International Conference on Telecommunications and Signal Processing (TSP), pp. 537–540 (2020). IEEE Redmon et al. [2016] Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016) Redmon and Farhadi [2018] Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018) Liu et al. [2016] Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21–37 (2016). Springer Girshick et al. [2014] Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) Girshick [2015] Girshick, R.: Fast R-CNN (2015) Ren et al. [2016] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. Computer Vision: A Reference Guide, 1–9 (2020) Contributors [2022] Contributors, M.: MMYOLO: OpenMMLab YOLO series toolbox and benchmark. https://github.com/open-mmlab/mmyolo (2022) Jocher et al. [2022] Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., Kwon, Y., Michael, K., Fang, J., Yifu, Z., Wong, C., Montes, D., et al.: ultralytics/yolov5: v7. 0-yolov5 sota realtime instance segmentation. Zenodo (2022) Jocher [2022] Jocher, G.: YOLOv5 v6.1 Release. https://community.ultralytics.com/t/yolov5-v6-1-release/51 (2022) Li et al. [2022] Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., Nie, W., Li, Y., Zhang, B., Liang, Y., Zhou, L., Xu, X., Chu, X., Wei, X., Wei, X.: YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications (2022) Li et al. [2023] Li, C., Li, L., Geng, Y., Jiang, H., Cheng, M., Zhang, B., Ke, Z., Xu, X., Chu, X.: YOLOv6 v3.0: A Full-Scale Reloading (2023) Wang et al. [2022] Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors (2022) Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics [32] What is YOLOv8? https://roboflow.com/model/yolov8 (2023) Aharon et al. [2021] Aharon, S., Louis-Dupont, Ofri Masad, Yurkova, K., Lotem Fridman, Lkdci, Khvedchenya, E., Rubin, R., Bagrov, N., Tymchenko, B., Keren, T., Zhilko, A., Eran-Deci: Super-Gradients. GitHub (2021). https://doi.org/10.5281/ZENODO.7789328 . https://zenodo.org/record/7789328 [34] YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Tapu, R., Mocanu, B., Zaharia, T.: Real time static/dynamic obstacle detection for visually impaired persons. In: 2014 IEEE International Conference on Consumer Electronics (ICCE), pp. 394–395 (2014). IEEE Elsken et al. [2019] Elsken, T., Metzen, J.H., Hutter, F.: Neural architecture search: A survey. The Journal of Machine Learning Research 20(1), 1997–2017 (2019) Said et al. [2023] Said, Y., Atri, M., Albahar, M.A., Ben Atitallah, A., Alsariera, Y.A.: Obstacle detection system for navigation assistance of visually impaired people based on deep learning techniques. Sensors 23(11), 5262 (2023) Rachburee and Punlumjeak [2021] Rachburee, N., Punlumjeak, W.: An assistive model of obstacle detection based on deep learning: Yolov3 for visually impaired people. International Journal of Electrical & Computer Engineering (2088-8708) 11(4) (2021) Yadav et al. [2020] Yadav, S., Joshi, R.C., Dutta, M.K., Kiac, M., Sikora, P.: Fusion of object recognition and obstacle detection approach for assisting visually challenged person. In: 2020 43rd International Conference on Telecommunications and Signal Processing (TSP), pp. 537–540 (2020). IEEE Redmon et al. [2016] Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016) Redmon and Farhadi [2018] Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018) Liu et al. [2016] Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21–37 (2016). Springer Girshick et al. [2014] Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) Girshick [2015] Girshick, R.: Fast R-CNN (2015) Ren et al. [2016] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. 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[2022] Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors (2022) Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics [32] What is YOLOv8? https://roboflow.com/model/yolov8 (2023) Aharon et al. [2021] Aharon, S., Louis-Dupont, Ofri Masad, Yurkova, K., Lotem Fridman, Lkdci, Khvedchenya, E., Rubin, R., Bagrov, N., Tymchenko, B., Keren, T., Zhilko, A., Eran-Deci: Super-Gradients. GitHub (2021). https://doi.org/10.5281/ZENODO.7789328 . https://zenodo.org/record/7789328 [34] YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Elsken, T., Metzen, J.H., Hutter, F.: Neural architecture search: A survey. The Journal of Machine Learning Research 20(1), 1997–2017 (2019) Said et al. [2023] Said, Y., Atri, M., Albahar, M.A., Ben Atitallah, A., Alsariera, Y.A.: Obstacle detection system for navigation assistance of visually impaired people based on deep learning techniques. Sensors 23(11), 5262 (2023) Rachburee and Punlumjeak [2021] Rachburee, N., Punlumjeak, W.: An assistive model of obstacle detection based on deep learning: Yolov3 for visually impaired people. International Journal of Electrical & Computer Engineering (2088-8708) 11(4) (2021) Yadav et al. [2020] Yadav, S., Joshi, R.C., Dutta, M.K., Kiac, M., Sikora, P.: Fusion of object recognition and obstacle detection approach for assisting visually challenged person. In: 2020 43rd International Conference on Telecommunications and Signal Processing (TSP), pp. 537–540 (2020). IEEE Redmon et al. [2016] Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016) Redmon and Farhadi [2018] Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018) Liu et al. [2016] Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21–37 (2016). Springer Girshick et al. [2014] Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) Girshick [2015] Girshick, R.: Fast R-CNN (2015) Ren et al. [2016] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. Computer Vision: A Reference Guide, 1–9 (2020) Contributors [2022] Contributors, M.: MMYOLO: OpenMMLab YOLO series toolbox and benchmark. https://github.com/open-mmlab/mmyolo (2022) Jocher et al. [2022] Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., Kwon, Y., Michael, K., Fang, J., Yifu, Z., Wong, C., Montes, D., et al.: ultralytics/yolov5: v7. 0-yolov5 sota realtime instance segmentation. Zenodo (2022) Jocher [2022] Jocher, G.: YOLOv5 v6.1 Release. https://community.ultralytics.com/t/yolov5-v6-1-release/51 (2022) Li et al. [2022] Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., Nie, W., Li, Y., Zhang, B., Liang, Y., Zhou, L., Xu, X., Chu, X., Wei, X., Wei, X.: YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications (2022) Li et al. [2023] Li, C., Li, L., Geng, Y., Jiang, H., Cheng, M., Zhang, B., Ke, Z., Xu, X., Chu, X.: YOLOv6 v3.0: A Full-Scale Reloading (2023) Wang et al. [2022] Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors (2022) Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics [32] What is YOLOv8? https://roboflow.com/model/yolov8 (2023) Aharon et al. [2021] Aharon, S., Louis-Dupont, Ofri Masad, Yurkova, K., Lotem Fridman, Lkdci, Khvedchenya, E., Rubin, R., Bagrov, N., Tymchenko, B., Keren, T., Zhilko, A., Eran-Deci: Super-Gradients. GitHub (2021). https://doi.org/10.5281/ZENODO.7789328 . https://zenodo.org/record/7789328 [34] YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Said, Y., Atri, M., Albahar, M.A., Ben Atitallah, A., Alsariera, Y.A.: Obstacle detection system for navigation assistance of visually impaired people based on deep learning techniques. Sensors 23(11), 5262 (2023) Rachburee and Punlumjeak [2021] Rachburee, N., Punlumjeak, W.: An assistive model of obstacle detection based on deep learning: Yolov3 for visually impaired people. International Journal of Electrical & Computer Engineering (2088-8708) 11(4) (2021) Yadav et al. [2020] Yadav, S., Joshi, R.C., Dutta, M.K., Kiac, M., Sikora, P.: Fusion of object recognition and obstacle detection approach for assisting visually challenged person. In: 2020 43rd International Conference on Telecommunications and Signal Processing (TSP), pp. 537–540 (2020). IEEE Redmon et al. [2016] Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016) Redmon and Farhadi [2018] Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018) Liu et al. [2016] Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21–37 (2016). Springer Girshick et al. [2014] Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) Girshick [2015] Girshick, R.: Fast R-CNN (2015) Ren et al. [2016] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. Computer Vision: A Reference Guide, 1–9 (2020) Contributors [2022] Contributors, M.: MMYOLO: OpenMMLab YOLO series toolbox and benchmark. https://github.com/open-mmlab/mmyolo (2022) Jocher et al. [2022] Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., Kwon, Y., Michael, K., Fang, J., Yifu, Z., Wong, C., Montes, D., et al.: ultralytics/yolov5: v7. 0-yolov5 sota realtime instance segmentation. Zenodo (2022) Jocher [2022] Jocher, G.: YOLOv5 v6.1 Release. https://community.ultralytics.com/t/yolov5-v6-1-release/51 (2022) Li et al. [2022] Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., Nie, W., Li, Y., Zhang, B., Liang, Y., Zhou, L., Xu, X., Chu, X., Wei, X., Wei, X.: YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications (2022) Li et al. [2023] Li, C., Li, L., Geng, Y., Jiang, H., Cheng, M., Zhang, B., Ke, Z., Xu, X., Chu, X.: YOLOv6 v3.0: A Full-Scale Reloading (2023) Wang et al. [2022] Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors (2022) Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics [32] What is YOLOv8? https://roboflow.com/model/yolov8 (2023) Aharon et al. [2021] Aharon, S., Louis-Dupont, Ofri Masad, Yurkova, K., Lotem Fridman, Lkdci, Khvedchenya, E., Rubin, R., Bagrov, N., Tymchenko, B., Keren, T., Zhilko, A., Eran-Deci: Super-Gradients. GitHub (2021). https://doi.org/10.5281/ZENODO.7789328 . https://zenodo.org/record/7789328 [34] YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Rachburee, N., Punlumjeak, W.: An assistive model of obstacle detection based on deep learning: Yolov3 for visually impaired people. International Journal of Electrical & Computer Engineering (2088-8708) 11(4) (2021) Yadav et al. [2020] Yadav, S., Joshi, R.C., Dutta, M.K., Kiac, M., Sikora, P.: Fusion of object recognition and obstacle detection approach for assisting visually challenged person. In: 2020 43rd International Conference on Telecommunications and Signal Processing (TSP), pp. 537–540 (2020). IEEE Redmon et al. [2016] Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016) Redmon and Farhadi [2018] Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018) Liu et al. [2016] Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21–37 (2016). Springer Girshick et al. [2014] Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) Girshick [2015] Girshick, R.: Fast R-CNN (2015) Ren et al. [2016] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. Computer Vision: A Reference Guide, 1–9 (2020) Contributors [2022] Contributors, M.: MMYOLO: OpenMMLab YOLO series toolbox and benchmark. https://github.com/open-mmlab/mmyolo (2022) Jocher et al. [2022] Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., Kwon, Y., Michael, K., Fang, J., Yifu, Z., Wong, C., Montes, D., et al.: ultralytics/yolov5: v7. 0-yolov5 sota realtime instance segmentation. Zenodo (2022) Jocher [2022] Jocher, G.: YOLOv5 v6.1 Release. https://community.ultralytics.com/t/yolov5-v6-1-release/51 (2022) Li et al. [2022] Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., Nie, W., Li, Y., Zhang, B., Liang, Y., Zhou, L., Xu, X., Chu, X., Wei, X., Wei, X.: YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications (2022) Li et al. [2023] Li, C., Li, L., Geng, Y., Jiang, H., Cheng, M., Zhang, B., Ke, Z., Xu, X., Chu, X.: YOLOv6 v3.0: A Full-Scale Reloading (2023) Wang et al. [2022] Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors (2022) Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics [32] What is YOLOv8? https://roboflow.com/model/yolov8 (2023) Aharon et al. [2021] Aharon, S., Louis-Dupont, Ofri Masad, Yurkova, K., Lotem Fridman, Lkdci, Khvedchenya, E., Rubin, R., Bagrov, N., Tymchenko, B., Keren, T., Zhilko, A., Eran-Deci: Super-Gradients. GitHub (2021). https://doi.org/10.5281/ZENODO.7789328 . https://zenodo.org/record/7789328 [34] YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Yadav, S., Joshi, R.C., Dutta, M.K., Kiac, M., Sikora, P.: Fusion of object recognition and obstacle detection approach for assisting visually challenged person. 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Algorithms 14(4), 114 (2021) Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016) Redmon and Farhadi [2018] Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018) Liu et al. [2016] Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21–37 (2016). Springer Girshick et al. [2014] Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) Girshick [2015] Girshick, R.: Fast R-CNN (2015) Ren et al. [2016] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. 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[2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. 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Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21–37 (2016). Springer Girshick et al. [2014] Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) Girshick [2015] Girshick, R.: Fast R-CNN (2015) Ren et al. 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GitHub (2021). https://doi.org/10.5281/ZENODO.7789328 . https://zenodo.org/record/7789328 [34] YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) Girshick [2015] Girshick, R.: Fast R-CNN (2015) Ren et al. [2016] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. 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[2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics [32] What is YOLOv8? https://roboflow.com/model/yolov8 (2023) Aharon et al. [2021] Aharon, S., Louis-Dupont, Ofri Masad, Yurkova, K., Lotem Fridman, Lkdci, Khvedchenya, E., Rubin, R., Bagrov, N., Tymchenko, B., Keren, T., Zhilko, A., Eran-Deci: Super-Gradients. GitHub (2021). https://doi.org/10.5281/ZENODO.7789328 . https://zenodo.org/record/7789328 [34] YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. 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[2016] Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21–37 (2016). Springer Girshick et al. [2014] Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) Girshick [2015] Girshick, R.: Fast R-CNN (2015) Ren et al. [2016] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. Computer Vision: A Reference Guide, 1–9 (2020) Contributors [2022] Contributors, M.: MMYOLO: OpenMMLab YOLO series toolbox and benchmark. https://github.com/open-mmlab/mmyolo (2022) Jocher et al. [2022] Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., Kwon, Y., Michael, K., Fang, J., Yifu, Z., Wong, C., Montes, D., et al.: ultralytics/yolov5: v7. 0-yolov5 sota realtime instance segmentation. Zenodo (2022) Jocher [2022] Jocher, G.: YOLOv5 v6.1 Release. https://community.ultralytics.com/t/yolov5-v6-1-release/51 (2022) Li et al. [2022] Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., Nie, W., Li, Y., Zhang, B., Liang, Y., Zhou, L., Xu, X., Chu, X., Wei, X., Wei, X.: YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications (2022) Li et al. [2023] Li, C., Li, L., Geng, Y., Jiang, H., Cheng, M., Zhang, B., Ke, Z., Xu, X., Chu, X.: YOLOv6 v3.0: A Full-Scale Reloading (2023) Wang et al. [2022] Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors (2022) Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics [32] What is YOLOv8? https://roboflow.com/model/yolov8 (2023) Aharon et al. [2021] Aharon, S., Louis-Dupont, Ofri Masad, Yurkova, K., Lotem Fridman, Lkdci, Khvedchenya, E., Rubin, R., Bagrov, N., Tymchenko, B., Keren, T., Zhilko, A., Eran-Deci: Super-Gradients. GitHub (2021). https://doi.org/10.5281/ZENODO.7789328 . https://zenodo.org/record/7789328 [34] YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Eckert, K.A., Carter, M.J., Lansingh, V.C., Wilson, D.A., Furtado, J.M., Frick, K.D., Resnikoff, S.: A simple method for estimating the economic cost of productivity loss due to blindness and moderate to severe visual impairment. Ophthalmic epidemiology 22(5), 349–355 (2015) Tapu et al. [2014] Tapu, R., Mocanu, B., Zaharia, T.: Real time static/dynamic obstacle detection for visually impaired persons. In: 2014 IEEE International Conference on Consumer Electronics (ICCE), pp. 394–395 (2014). IEEE Elsken et al. [2019] Elsken, T., Metzen, J.H., Hutter, F.: Neural architecture search: A survey. The Journal of Machine Learning Research 20(1), 1997–2017 (2019) Said et al. [2023] Said, Y., Atri, M., Albahar, M.A., Ben Atitallah, A., Alsariera, Y.A.: Obstacle detection system for navigation assistance of visually impaired people based on deep learning techniques. Sensors 23(11), 5262 (2023) Rachburee and Punlumjeak [2021] Rachburee, N., Punlumjeak, W.: An assistive model of obstacle detection based on deep learning: Yolov3 for visually impaired people. International Journal of Electrical & Computer Engineering (2088-8708) 11(4) (2021) Yadav et al. [2020] Yadav, S., Joshi, R.C., Dutta, M.K., Kiac, M., Sikora, P.: Fusion of object recognition and obstacle detection approach for assisting visually challenged person. In: 2020 43rd International Conference on Telecommunications and Signal Processing (TSP), pp. 537–540 (2020). IEEE Redmon et al. [2016] Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016) Redmon and Farhadi [2018] Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018) Liu et al. [2016] Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21–37 (2016). Springer Girshick et al. [2014] Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) Girshick [2015] Girshick, R.: Fast R-CNN (2015) Ren et al. [2016] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. 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[2016] Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016) Redmon and Farhadi [2018] Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018) Liu et al. [2016] Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21–37 (2016). Springer Girshick et al. [2014] Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) Girshick [2015] Girshick, R.: Fast R-CNN (2015) Ren et al. [2016] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. Computer Vision: A Reference Guide, 1–9 (2020) Contributors [2022] Contributors, M.: MMYOLO: OpenMMLab YOLO series toolbox and benchmark. https://github.com/open-mmlab/mmyolo (2022) Jocher et al. [2022] Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., Kwon, Y., Michael, K., Fang, J., Yifu, Z., Wong, C., Montes, D., et al.: ultralytics/yolov5: v7. 0-yolov5 sota realtime instance segmentation. Zenodo (2022) Jocher [2022] Jocher, G.: YOLOv5 v6.1 Release. https://community.ultralytics.com/t/yolov5-v6-1-release/51 (2022) Li et al. [2022] Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., Nie, W., Li, Y., Zhang, B., Liang, Y., Zhou, L., Xu, X., Chu, X., Wei, X., Wei, X.: YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications (2022) Li et al. [2023] Li, C., Li, L., Geng, Y., Jiang, H., Cheng, M., Zhang, B., Ke, Z., Xu, X., Chu, X.: YOLOv6 v3.0: A Full-Scale Reloading (2023) Wang et al. [2022] Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors (2022) Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics [32] What is YOLOv8? https://roboflow.com/model/yolov8 (2023) Aharon et al. [2021] Aharon, S., Louis-Dupont, Ofri Masad, Yurkova, K., Lotem Fridman, Lkdci, Khvedchenya, E., Rubin, R., Bagrov, N., Tymchenko, B., Keren, T., Zhilko, A., Eran-Deci: Super-Gradients. GitHub (2021). https://doi.org/10.5281/ZENODO.7789328 . https://zenodo.org/record/7789328 [34] YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Elsken, T., Metzen, J.H., Hutter, F.: Neural architecture search: A survey. The Journal of Machine Learning Research 20(1), 1997–2017 (2019) Said et al. [2023] Said, Y., Atri, M., Albahar, M.A., Ben Atitallah, A., Alsariera, Y.A.: Obstacle detection system for navigation assistance of visually impaired people based on deep learning techniques. Sensors 23(11), 5262 (2023) Rachburee and Punlumjeak [2021] Rachburee, N., Punlumjeak, W.: An assistive model of obstacle detection based on deep learning: Yolov3 for visually impaired people. International Journal of Electrical & Computer Engineering (2088-8708) 11(4) (2021) Yadav et al. [2020] Yadav, S., Joshi, R.C., Dutta, M.K., Kiac, M., Sikora, P.: Fusion of object recognition and obstacle detection approach for assisting visually challenged person. In: 2020 43rd International Conference on Telecommunications and Signal Processing (TSP), pp. 537–540 (2020). IEEE Redmon et al. [2016] Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016) Redmon and Farhadi [2018] Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018) Liu et al. [2016] Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21–37 (2016). Springer Girshick et al. [2014] Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) Girshick [2015] Girshick, R.: Fast R-CNN (2015) Ren et al. [2016] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. Computer Vision: A Reference Guide, 1–9 (2020) Contributors [2022] Contributors, M.: MMYOLO: OpenMMLab YOLO series toolbox and benchmark. https://github.com/open-mmlab/mmyolo (2022) Jocher et al. [2022] Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., Kwon, Y., Michael, K., Fang, J., Yifu, Z., Wong, C., Montes, D., et al.: ultralytics/yolov5: v7. 0-yolov5 sota realtime instance segmentation. Zenodo (2022) Jocher [2022] Jocher, G.: YOLOv5 v6.1 Release. https://community.ultralytics.com/t/yolov5-v6-1-release/51 (2022) Li et al. [2022] Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., Nie, W., Li, Y., Zhang, B., Liang, Y., Zhou, L., Xu, X., Chu, X., Wei, X., Wei, X.: YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications (2022) Li et al. [2023] Li, C., Li, L., Geng, Y., Jiang, H., Cheng, M., Zhang, B., Ke, Z., Xu, X., Chu, X.: YOLOv6 v3.0: A Full-Scale Reloading (2023) Wang et al. [2022] Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors (2022) Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics [32] What is YOLOv8? https://roboflow.com/model/yolov8 (2023) Aharon et al. [2021] Aharon, S., Louis-Dupont, Ofri Masad, Yurkova, K., Lotem Fridman, Lkdci, Khvedchenya, E., Rubin, R., Bagrov, N., Tymchenko, B., Keren, T., Zhilko, A., Eran-Deci: Super-Gradients. GitHub (2021). https://doi.org/10.5281/ZENODO.7789328 . https://zenodo.org/record/7789328 [34] YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Said, Y., Atri, M., Albahar, M.A., Ben Atitallah, A., Alsariera, Y.A.: Obstacle detection system for navigation assistance of visually impaired people based on deep learning techniques. Sensors 23(11), 5262 (2023) Rachburee and Punlumjeak [2021] Rachburee, N., Punlumjeak, W.: An assistive model of obstacle detection based on deep learning: Yolov3 for visually impaired people. International Journal of Electrical & Computer Engineering (2088-8708) 11(4) (2021) Yadav et al. [2020] Yadav, S., Joshi, R.C., Dutta, M.K., Kiac, M., Sikora, P.: Fusion of object recognition and obstacle detection approach for assisting visually challenged person. In: 2020 43rd International Conference on Telecommunications and Signal Processing (TSP), pp. 537–540 (2020). IEEE Redmon et al. [2016] Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016) Redmon and Farhadi [2018] Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018) Liu et al. [2016] Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21–37 (2016). Springer Girshick et al. [2014] Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) Girshick [2015] Girshick, R.: Fast R-CNN (2015) Ren et al. [2016] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. Computer Vision: A Reference Guide, 1–9 (2020) Contributors [2022] Contributors, M.: MMYOLO: OpenMMLab YOLO series toolbox and benchmark. https://github.com/open-mmlab/mmyolo (2022) Jocher et al. [2022] Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., Kwon, Y., Michael, K., Fang, J., Yifu, Z., Wong, C., Montes, D., et al.: ultralytics/yolov5: v7. 0-yolov5 sota realtime instance segmentation. Zenodo (2022) Jocher [2022] Jocher, G.: YOLOv5 v6.1 Release. https://community.ultralytics.com/t/yolov5-v6-1-release/51 (2022) Li et al. [2022] Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., Nie, W., Li, Y., Zhang, B., Liang, Y., Zhou, L., Xu, X., Chu, X., Wei, X., Wei, X.: YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications (2022) Li et al. [2023] Li, C., Li, L., Geng, Y., Jiang, H., Cheng, M., Zhang, B., Ke, Z., Xu, X., Chu, X.: YOLOv6 v3.0: A Full-Scale Reloading (2023) Wang et al. [2022] Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors (2022) Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics [32] What is YOLOv8? https://roboflow.com/model/yolov8 (2023) Aharon et al. [2021] Aharon, S., Louis-Dupont, Ofri Masad, Yurkova, K., Lotem Fridman, Lkdci, Khvedchenya, E., Rubin, R., Bagrov, N., Tymchenko, B., Keren, T., Zhilko, A., Eran-Deci: Super-Gradients. GitHub (2021). https://doi.org/10.5281/ZENODO.7789328 . https://zenodo.org/record/7789328 [34] YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Rachburee, N., Punlumjeak, W.: An assistive model of obstacle detection based on deep learning: Yolov3 for visually impaired people. International Journal of Electrical & Computer Engineering (2088-8708) 11(4) (2021) Yadav et al. [2020] Yadav, S., Joshi, R.C., Dutta, M.K., Kiac, M., Sikora, P.: Fusion of object recognition and obstacle detection approach for assisting visually challenged person. In: 2020 43rd International Conference on Telecommunications and Signal Processing (TSP), pp. 537–540 (2020). IEEE Redmon et al. [2016] Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016) Redmon and Farhadi [2018] Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018) Liu et al. [2016] Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21–37 (2016). Springer Girshick et al. [2014] Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) Girshick [2015] Girshick, R.: Fast R-CNN (2015) Ren et al. [2016] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. 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In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) Girshick [2015] Girshick, R.: Fast R-CNN (2015) Ren et al. [2016] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. Computer Vision: A Reference Guide, 1–9 (2020) Contributors [2022] Contributors, M.: MMYOLO: OpenMMLab YOLO series toolbox and benchmark. https://github.com/open-mmlab/mmyolo (2022) Jocher et al. [2022] Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., Kwon, Y., Michael, K., Fang, J., Yifu, Z., Wong, C., Montes, D., et al.: ultralytics/yolov5: v7. 0-yolov5 sota realtime instance segmentation. 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[2021] Aharon, S., Louis-Dupont, Ofri Masad, Yurkova, K., Lotem Fridman, Lkdci, Khvedchenya, E., Rubin, R., Bagrov, N., Tymchenko, B., Keren, T., Zhilko, A., Eran-Deci: Super-Gradients. GitHub (2021). https://doi.org/10.5281/ZENODO.7789328 . https://zenodo.org/record/7789328 [34] YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016) Redmon and Farhadi [2018] Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018) Liu et al. [2016] Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21–37 (2016). Springer Girshick et al. [2014] Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) Girshick [2015] Girshick, R.: Fast R-CNN (2015) Ren et al. [2016] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. Computer Vision: A Reference Guide, 1–9 (2020) Contributors [2022] Contributors, M.: MMYOLO: OpenMMLab YOLO series toolbox and benchmark. https://github.com/open-mmlab/mmyolo (2022) Jocher et al. [2022] Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., Kwon, Y., Michael, K., Fang, J., Yifu, Z., Wong, C., Montes, D., et al.: ultralytics/yolov5: v7. 0-yolov5 sota realtime instance segmentation. Zenodo (2022) Jocher [2022] Jocher, G.: YOLOv5 v6.1 Release. https://community.ultralytics.com/t/yolov5-v6-1-release/51 (2022) Li et al. 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In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21–37 (2016). Springer Girshick et al. [2014] Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) Girshick [2015] Girshick, R.: Fast R-CNN (2015) Ren et al. [2016] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. 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[2022] Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors (2022) Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics [32] What is YOLOv8? https://roboflow.com/model/yolov8 (2023) Aharon et al. [2021] Aharon, S., Louis-Dupont, Ofri Masad, Yurkova, K., Lotem Fridman, Lkdci, Khvedchenya, E., Rubin, R., Bagrov, N., Tymchenko, B., Keren, T., Zhilko, A., Eran-Deci: Super-Gradients. GitHub (2021). https://doi.org/10.5281/ZENODO.7789328 . https://zenodo.org/record/7789328 [34] YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21–37 (2016). Springer Girshick et al. [2014] Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) Girshick [2015] Girshick, R.: Fast R-CNN (2015) Ren et al. [2016] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. Computer Vision: A Reference Guide, 1–9 (2020) Contributors [2022] Contributors, M.: MMYOLO: OpenMMLab YOLO series toolbox and benchmark. https://github.com/open-mmlab/mmyolo (2022) Jocher et al. [2022] Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., Kwon, Y., Michael, K., Fang, J., Yifu, Z., Wong, C., Montes, D., et al.: ultralytics/yolov5: v7. 0-yolov5 sota realtime instance segmentation. Zenodo (2022) Jocher [2022] Jocher, G.: YOLOv5 v6.1 Release. https://community.ultralytics.com/t/yolov5-v6-1-release/51 (2022) Li et al. 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International Journal of Electrical & Computer Engineering (2088-8708) 11(4) (2021) Yadav et al. [2020] Yadav, S., Joshi, R.C., Dutta, M.K., Kiac, M., Sikora, P.: Fusion of object recognition and obstacle detection approach for assisting visually challenged person. In: 2020 43rd International Conference on Telecommunications and Signal Processing (TSP), pp. 537–540 (2020). IEEE Redmon et al. [2016] Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016) Redmon and Farhadi [2018] Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018) Liu et al. [2016] Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21–37 (2016). Springer Girshick et al. [2014] Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) Girshick [2015] Girshick, R.: Fast R-CNN (2015) Ren et al. [2016] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. 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[2022] Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors (2022) Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics [32] What is YOLOv8? https://roboflow.com/model/yolov8 (2023) Aharon et al. [2021] Aharon, S., Louis-Dupont, Ofri Masad, Yurkova, K., Lotem Fridman, Lkdci, Khvedchenya, E., Rubin, R., Bagrov, N., Tymchenko, B., Keren, T., Zhilko, A., Eran-Deci: Super-Gradients. GitHub (2021). https://doi.org/10.5281/ZENODO.7789328 . https://zenodo.org/record/7789328 [34] YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Elsken, T., Metzen, J.H., Hutter, F.: Neural architecture search: A survey. The Journal of Machine Learning Research 20(1), 1997–2017 (2019) Said et al. [2023] Said, Y., Atri, M., Albahar, M.A., Ben Atitallah, A., Alsariera, Y.A.: Obstacle detection system for navigation assistance of visually impaired people based on deep learning techniques. Sensors 23(11), 5262 (2023) Rachburee and Punlumjeak [2021] Rachburee, N., Punlumjeak, W.: An assistive model of obstacle detection based on deep learning: Yolov3 for visually impaired people. International Journal of Electrical & Computer Engineering (2088-8708) 11(4) (2021) Yadav et al. [2020] Yadav, S., Joshi, R.C., Dutta, M.K., Kiac, M., Sikora, P.: Fusion of object recognition and obstacle detection approach for assisting visually challenged person. In: 2020 43rd International Conference on Telecommunications and Signal Processing (TSP), pp. 537–540 (2020). IEEE Redmon et al. [2016] Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016) Redmon and Farhadi [2018] Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018) Liu et al. [2016] Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21–37 (2016). Springer Girshick et al. [2014] Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) Girshick [2015] Girshick, R.: Fast R-CNN (2015) Ren et al. [2016] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. Computer Vision: A Reference Guide, 1–9 (2020) Contributors [2022] Contributors, M.: MMYOLO: OpenMMLab YOLO series toolbox and benchmark. https://github.com/open-mmlab/mmyolo (2022) Jocher et al. [2022] Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., Kwon, Y., Michael, K., Fang, J., Yifu, Z., Wong, C., Montes, D., et al.: ultralytics/yolov5: v7. 0-yolov5 sota realtime instance segmentation. Zenodo (2022) Jocher [2022] Jocher, G.: YOLOv5 v6.1 Release. https://community.ultralytics.com/t/yolov5-v6-1-release/51 (2022) Li et al. [2022] Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., Nie, W., Li, Y., Zhang, B., Liang, Y., Zhou, L., Xu, X., Chu, X., Wei, X., Wei, X.: YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications (2022) Li et al. [2023] Li, C., Li, L., Geng, Y., Jiang, H., Cheng, M., Zhang, B., Ke, Z., Xu, X., Chu, X.: YOLOv6 v3.0: A Full-Scale Reloading (2023) Wang et al. [2022] Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors (2022) Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics [32] What is YOLOv8? https://roboflow.com/model/yolov8 (2023) Aharon et al. [2021] Aharon, S., Louis-Dupont, Ofri Masad, Yurkova, K., Lotem Fridman, Lkdci, Khvedchenya, E., Rubin, R., Bagrov, N., Tymchenko, B., Keren, T., Zhilko, A., Eran-Deci: Super-Gradients. GitHub (2021). https://doi.org/10.5281/ZENODO.7789328 . https://zenodo.org/record/7789328 [34] YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Said, Y., Atri, M., Albahar, M.A., Ben Atitallah, A., Alsariera, Y.A.: Obstacle detection system for navigation assistance of visually impaired people based on deep learning techniques. Sensors 23(11), 5262 (2023) Rachburee and Punlumjeak [2021] Rachburee, N., Punlumjeak, W.: An assistive model of obstacle detection based on deep learning: Yolov3 for visually impaired people. International Journal of Electrical & Computer Engineering (2088-8708) 11(4) (2021) Yadav et al. [2020] Yadav, S., Joshi, R.C., Dutta, M.K., Kiac, M., Sikora, P.: Fusion of object recognition and obstacle detection approach for assisting visually challenged person. In: 2020 43rd International Conference on Telecommunications and Signal Processing (TSP), pp. 537–540 (2020). IEEE Redmon et al. [2016] Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016) Redmon and Farhadi [2018] Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018) Liu et al. [2016] Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21–37 (2016). Springer Girshick et al. [2014] Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) Girshick [2015] Girshick, R.: Fast R-CNN (2015) Ren et al. [2016] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. Computer Vision: A Reference Guide, 1–9 (2020) Contributors [2022] Contributors, M.: MMYOLO: OpenMMLab YOLO series toolbox and benchmark. https://github.com/open-mmlab/mmyolo (2022) Jocher et al. [2022] Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., Kwon, Y., Michael, K., Fang, J., Yifu, Z., Wong, C., Montes, D., et al.: ultralytics/yolov5: v7. 0-yolov5 sota realtime instance segmentation. Zenodo (2022) Jocher [2022] Jocher, G.: YOLOv5 v6.1 Release. https://community.ultralytics.com/t/yolov5-v6-1-release/51 (2022) Li et al. [2022] Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., Nie, W., Li, Y., Zhang, B., Liang, Y., Zhou, L., Xu, X., Chu, X., Wei, X., Wei, X.: YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications (2022) Li et al. [2023] Li, C., Li, L., Geng, Y., Jiang, H., Cheng, M., Zhang, B., Ke, Z., Xu, X., Chu, X.: YOLOv6 v3.0: A Full-Scale Reloading (2023) Wang et al. [2022] Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors (2022) Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics [32] What is YOLOv8? https://roboflow.com/model/yolov8 (2023) Aharon et al. [2021] Aharon, S., Louis-Dupont, Ofri Masad, Yurkova, K., Lotem Fridman, Lkdci, Khvedchenya, E., Rubin, R., Bagrov, N., Tymchenko, B., Keren, T., Zhilko, A., Eran-Deci: Super-Gradients. GitHub (2021). https://doi.org/10.5281/ZENODO.7789328 . https://zenodo.org/record/7789328 [34] YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Rachburee, N., Punlumjeak, W.: An assistive model of obstacle detection based on deep learning: Yolov3 for visually impaired people. International Journal of Electrical & Computer Engineering (2088-8708) 11(4) (2021) Yadav et al. [2020] Yadav, S., Joshi, R.C., Dutta, M.K., Kiac, M., Sikora, P.: Fusion of object recognition and obstacle detection approach for assisting visually challenged person. In: 2020 43rd International Conference on Telecommunications and Signal Processing (TSP), pp. 537–540 (2020). IEEE Redmon et al. [2016] Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016) Redmon and Farhadi [2018] Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018) Liu et al. [2016] Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21–37 (2016). Springer Girshick et al. [2014] Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) Girshick [2015] Girshick, R.: Fast R-CNN (2015) Ren et al. [2016] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. Computer Vision: A Reference Guide, 1–9 (2020) Contributors [2022] Contributors, M.: MMYOLO: OpenMMLab YOLO series toolbox and benchmark. https://github.com/open-mmlab/mmyolo (2022) Jocher et al. [2022] Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., Kwon, Y., Michael, K., Fang, J., Yifu, Z., Wong, C., Montes, D., et al.: ultralytics/yolov5: v7. 0-yolov5 sota realtime instance segmentation. Zenodo (2022) Jocher [2022] Jocher, G.: YOLOv5 v6.1 Release. https://community.ultralytics.com/t/yolov5-v6-1-release/51 (2022) Li et al. [2022] Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., Nie, W., Li, Y., Zhang, B., Liang, Y., Zhou, L., Xu, X., Chu, X., Wei, X., Wei, X.: YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications (2022) Li et al. [2023] Li, C., Li, L., Geng, Y., Jiang, H., Cheng, M., Zhang, B., Ke, Z., Xu, X., Chu, X.: YOLOv6 v3.0: A Full-Scale Reloading (2023) Wang et al. [2022] Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors (2022) Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics [32] What is YOLOv8? https://roboflow.com/model/yolov8 (2023) Aharon et al. [2021] Aharon, S., Louis-Dupont, Ofri Masad, Yurkova, K., Lotem Fridman, Lkdci, Khvedchenya, E., Rubin, R., Bagrov, N., Tymchenko, B., Keren, T., Zhilko, A., Eran-Deci: Super-Gradients. GitHub (2021). https://doi.org/10.5281/ZENODO.7789328 . https://zenodo.org/record/7789328 [34] YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Yadav, S., Joshi, R.C., Dutta, M.K., Kiac, M., Sikora, P.: Fusion of object recognition and obstacle detection approach for assisting visually challenged person. 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Algorithms 14(4), 114 (2021) Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016) Redmon and Farhadi [2018] Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018) Liu et al. [2016] Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21–37 (2016). Springer Girshick et al. [2014] Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) Girshick [2015] Girshick, R.: Fast R-CNN (2015) Ren et al. [2016] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. 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[2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. 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Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21–37 (2016). Springer Girshick et al. [2014] Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) Girshick [2015] Girshick, R.: Fast R-CNN (2015) Ren et al. 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GitHub (2021). https://doi.org/10.5281/ZENODO.7789328 . https://zenodo.org/record/7789328 [34] YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) Girshick [2015] Girshick, R.: Fast R-CNN (2015) Ren et al. [2016] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. 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[2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics [32] What is YOLOv8? https://roboflow.com/model/yolov8 (2023) Aharon et al. [2021] Aharon, S., Louis-Dupont, Ofri Masad, Yurkova, K., Lotem Fridman, Lkdci, Khvedchenya, E., Rubin, R., Bagrov, N., Tymchenko, B., Keren, T., Zhilko, A., Eran-Deci: Super-Gradients. GitHub (2021). https://doi.org/10.5281/ZENODO.7789328 . https://zenodo.org/record/7789328 [34] YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. 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Computer Vision: A Reference Guide, 1–9 (2020) Contributors [2022] Contributors, M.: MMYOLO: OpenMMLab YOLO series toolbox and benchmark. https://github.com/open-mmlab/mmyolo (2022) Jocher et al. [2022] Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., Kwon, Y., Michael, K., Fang, J., Yifu, Z., Wong, C., Montes, D., et al.: ultralytics/yolov5: v7. 0-yolov5 sota realtime instance segmentation. Zenodo (2022) Jocher [2022] Jocher, G.: YOLOv5 v6.1 Release. https://community.ultralytics.com/t/yolov5-v6-1-release/51 (2022) Li et al. [2022] Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., Nie, W., Li, Y., Zhang, B., Liang, Y., Zhou, L., Xu, X., Chu, X., Wei, X., Wei, X.: YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications (2022) Li et al. [2023] Li, C., Li, L., Geng, Y., Jiang, H., Cheng, M., Zhang, B., Ke, Z., Xu, X., Chu, X.: YOLOv6 v3.0: A Full-Scale Reloading (2023) Wang et al. [2022] Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors (2022) Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics [32] What is YOLOv8? https://roboflow.com/model/yolov8 (2023) Aharon et al. [2021] Aharon, S., Louis-Dupont, Ofri Masad, Yurkova, K., Lotem Fridman, Lkdci, Khvedchenya, E., Rubin, R., Bagrov, N., Tymchenko, B., Keren, T., Zhilko, A., Eran-Deci: Super-Gradients. GitHub (2021). https://doi.org/10.5281/ZENODO.7789328 . https://zenodo.org/record/7789328 [34] YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Tapu, R., Mocanu, B., Zaharia, T.: Real time static/dynamic obstacle detection for visually impaired persons. In: 2014 IEEE International Conference on Consumer Electronics (ICCE), pp. 394–395 (2014). IEEE Elsken et al. [2019] Elsken, T., Metzen, J.H., Hutter, F.: Neural architecture search: A survey. The Journal of Machine Learning Research 20(1), 1997–2017 (2019) Said et al. [2023] Said, Y., Atri, M., Albahar, M.A., Ben Atitallah, A., Alsariera, Y.A.: Obstacle detection system for navigation assistance of visually impaired people based on deep learning techniques. Sensors 23(11), 5262 (2023) Rachburee and Punlumjeak [2021] Rachburee, N., Punlumjeak, W.: An assistive model of obstacle detection based on deep learning: Yolov3 for visually impaired people. International Journal of Electrical & Computer Engineering (2088-8708) 11(4) (2021) Yadav et al. [2020] Yadav, S., Joshi, R.C., Dutta, M.K., Kiac, M., Sikora, P.: Fusion of object recognition and obstacle detection approach for assisting visually challenged person. In: 2020 43rd International Conference on Telecommunications and Signal Processing (TSP), pp. 537–540 (2020). IEEE Redmon et al. [2016] Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016) Redmon and Farhadi [2018] Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018) Liu et al. [2016] Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21–37 (2016). Springer Girshick et al. [2014] Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) Girshick [2015] Girshick, R.: Fast R-CNN (2015) Ren et al. [2016] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. Computer Vision: A Reference Guide, 1–9 (2020) Contributors [2022] Contributors, M.: MMYOLO: OpenMMLab YOLO series toolbox and benchmark. https://github.com/open-mmlab/mmyolo (2022) Jocher et al. [2022] Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., Kwon, Y., Michael, K., Fang, J., Yifu, Z., Wong, C., Montes, D., et al.: ultralytics/yolov5: v7. 0-yolov5 sota realtime instance segmentation. Zenodo (2022) Jocher [2022] Jocher, G.: YOLOv5 v6.1 Release. https://community.ultralytics.com/t/yolov5-v6-1-release/51 (2022) Li et al. [2022] Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., Nie, W., Li, Y., Zhang, B., Liang, Y., Zhou, L., Xu, X., Chu, X., Wei, X., Wei, X.: YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications (2022) Li et al. [2023] Li, C., Li, L., Geng, Y., Jiang, H., Cheng, M., Zhang, B., Ke, Z., Xu, X., Chu, X.: YOLOv6 v3.0: A Full-Scale Reloading (2023) Wang et al. [2022] Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors (2022) Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics [32] What is YOLOv8? https://roboflow.com/model/yolov8 (2023) Aharon et al. [2021] Aharon, S., Louis-Dupont, Ofri Masad, Yurkova, K., Lotem Fridman, Lkdci, Khvedchenya, E., Rubin, R., Bagrov, N., Tymchenko, B., Keren, T., Zhilko, A., Eran-Deci: Super-Gradients. GitHub (2021). https://doi.org/10.5281/ZENODO.7789328 . https://zenodo.org/record/7789328 [34] YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Elsken, T., Metzen, J.H., Hutter, F.: Neural architecture search: A survey. The Journal of Machine Learning Research 20(1), 1997–2017 (2019) Said et al. [2023] Said, Y., Atri, M., Albahar, M.A., Ben Atitallah, A., Alsariera, Y.A.: Obstacle detection system for navigation assistance of visually impaired people based on deep learning techniques. Sensors 23(11), 5262 (2023) Rachburee and Punlumjeak [2021] Rachburee, N., Punlumjeak, W.: An assistive model of obstacle detection based on deep learning: Yolov3 for visually impaired people. International Journal of Electrical & Computer Engineering (2088-8708) 11(4) (2021) Yadav et al. [2020] Yadav, S., Joshi, R.C., Dutta, M.K., Kiac, M., Sikora, P.: Fusion of object recognition and obstacle detection approach for assisting visually challenged person. In: 2020 43rd International Conference on Telecommunications and Signal Processing (TSP), pp. 537–540 (2020). IEEE Redmon et al. [2016] Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016) Redmon and Farhadi [2018] Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018) Liu et al. [2016] Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21–37 (2016). Springer Girshick et al. [2014] Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) Girshick [2015] Girshick, R.: Fast R-CNN (2015) Ren et al. [2016] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. Computer Vision: A Reference Guide, 1–9 (2020) Contributors [2022] Contributors, M.: MMYOLO: OpenMMLab YOLO series toolbox and benchmark. https://github.com/open-mmlab/mmyolo (2022) Jocher et al. [2022] Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., Kwon, Y., Michael, K., Fang, J., Yifu, Z., Wong, C., Montes, D., et al.: ultralytics/yolov5: v7. 0-yolov5 sota realtime instance segmentation. Zenodo (2022) Jocher [2022] Jocher, G.: YOLOv5 v6.1 Release. https://community.ultralytics.com/t/yolov5-v6-1-release/51 (2022) Li et al. [2022] Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., Nie, W., Li, Y., Zhang, B., Liang, Y., Zhou, L., Xu, X., Chu, X., Wei, X., Wei, X.: YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications (2022) Li et al. [2023] Li, C., Li, L., Geng, Y., Jiang, H., Cheng, M., Zhang, B., Ke, Z., Xu, X., Chu, X.: YOLOv6 v3.0: A Full-Scale Reloading (2023) Wang et al. [2022] Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors (2022) Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics [32] What is YOLOv8? https://roboflow.com/model/yolov8 (2023) Aharon et al. [2021] Aharon, S., Louis-Dupont, Ofri Masad, Yurkova, K., Lotem Fridman, Lkdci, Khvedchenya, E., Rubin, R., Bagrov, N., Tymchenko, B., Keren, T., Zhilko, A., Eran-Deci: Super-Gradients. GitHub (2021). https://doi.org/10.5281/ZENODO.7789328 . https://zenodo.org/record/7789328 [34] YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Said, Y., Atri, M., Albahar, M.A., Ben Atitallah, A., Alsariera, Y.A.: Obstacle detection system for navigation assistance of visually impaired people based on deep learning techniques. Sensors 23(11), 5262 (2023) Rachburee and Punlumjeak [2021] Rachburee, N., Punlumjeak, W.: An assistive model of obstacle detection based on deep learning: Yolov3 for visually impaired people. International Journal of Electrical & Computer Engineering (2088-8708) 11(4) (2021) Yadav et al. [2020] Yadav, S., Joshi, R.C., Dutta, M.K., Kiac, M., Sikora, P.: Fusion of object recognition and obstacle detection approach for assisting visually challenged person. In: 2020 43rd International Conference on Telecommunications and Signal Processing (TSP), pp. 537–540 (2020). IEEE Redmon et al. [2016] Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016) Redmon and Farhadi [2018] Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018) Liu et al. [2016] Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21–37 (2016). Springer Girshick et al. [2014] Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) Girshick [2015] Girshick, R.: Fast R-CNN (2015) Ren et al. [2016] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. Computer Vision: A Reference Guide, 1–9 (2020) Contributors [2022] Contributors, M.: MMYOLO: OpenMMLab YOLO series toolbox and benchmark. https://github.com/open-mmlab/mmyolo (2022) Jocher et al. [2022] Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., Kwon, Y., Michael, K., Fang, J., Yifu, Z., Wong, C., Montes, D., et al.: ultralytics/yolov5: v7. 0-yolov5 sota realtime instance segmentation. Zenodo (2022) Jocher [2022] Jocher, G.: YOLOv5 v6.1 Release. https://community.ultralytics.com/t/yolov5-v6-1-release/51 (2022) Li et al. [2022] Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., Nie, W., Li, Y., Zhang, B., Liang, Y., Zhou, L., Xu, X., Chu, X., Wei, X., Wei, X.: YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications (2022) Li et al. [2023] Li, C., Li, L., Geng, Y., Jiang, H., Cheng, M., Zhang, B., Ke, Z., Xu, X., Chu, X.: YOLOv6 v3.0: A Full-Scale Reloading (2023) Wang et al. [2022] Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors (2022) Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics [32] What is YOLOv8? https://roboflow.com/model/yolov8 (2023) Aharon et al. [2021] Aharon, S., Louis-Dupont, Ofri Masad, Yurkova, K., Lotem Fridman, Lkdci, Khvedchenya, E., Rubin, R., Bagrov, N., Tymchenko, B., Keren, T., Zhilko, A., Eran-Deci: Super-Gradients. GitHub (2021). https://doi.org/10.5281/ZENODO.7789328 . https://zenodo.org/record/7789328 [34] YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Rachburee, N., Punlumjeak, W.: An assistive model of obstacle detection based on deep learning: Yolov3 for visually impaired people. International Journal of Electrical & Computer Engineering (2088-8708) 11(4) (2021) Yadav et al. [2020] Yadav, S., Joshi, R.C., Dutta, M.K., Kiac, M., Sikora, P.: Fusion of object recognition and obstacle detection approach for assisting visually challenged person. In: 2020 43rd International Conference on Telecommunications and Signal Processing (TSP), pp. 537–540 (2020). IEEE Redmon et al. [2016] Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016) Redmon and Farhadi [2018] Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018) Liu et al. [2016] Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21–37 (2016). Springer Girshick et al. [2014] Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) Girshick [2015] Girshick, R.: Fast R-CNN (2015) Ren et al. [2016] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. Computer Vision: A Reference Guide, 1–9 (2020) Contributors [2022] Contributors, M.: MMYOLO: OpenMMLab YOLO series toolbox and benchmark. https://github.com/open-mmlab/mmyolo (2022) Jocher et al. [2022] Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., Kwon, Y., Michael, K., Fang, J., Yifu, Z., Wong, C., Montes, D., et al.: ultralytics/yolov5: v7. 0-yolov5 sota realtime instance segmentation. Zenodo (2022) Jocher [2022] Jocher, G.: YOLOv5 v6.1 Release. https://community.ultralytics.com/t/yolov5-v6-1-release/51 (2022) Li et al. [2022] Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., Nie, W., Li, Y., Zhang, B., Liang, Y., Zhou, L., Xu, X., Chu, X., Wei, X., Wei, X.: YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications (2022) Li et al. [2023] Li, C., Li, L., Geng, Y., Jiang, H., Cheng, M., Zhang, B., Ke, Z., Xu, X., Chu, X.: YOLOv6 v3.0: A Full-Scale Reloading (2023) Wang et al. [2022] Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors (2022) Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics [32] What is YOLOv8? https://roboflow.com/model/yolov8 (2023) Aharon et al. [2021] Aharon, S., Louis-Dupont, Ofri Masad, Yurkova, K., Lotem Fridman, Lkdci, Khvedchenya, E., Rubin, R., Bagrov, N., Tymchenko, B., Keren, T., Zhilko, A., Eran-Deci: Super-Gradients. GitHub (2021). https://doi.org/10.5281/ZENODO.7789328 . https://zenodo.org/record/7789328 [34] YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Yadav, S., Joshi, R.C., Dutta, M.K., Kiac, M., Sikora, P.: Fusion of object recognition and obstacle detection approach for assisting visually challenged person. In: 2020 43rd International Conference on Telecommunications and Signal Processing (TSP), pp. 537–540 (2020). IEEE Redmon et al. [2016] Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016) Redmon and Farhadi [2018] Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018) Liu et al. [2016] Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21–37 (2016). Springer Girshick et al. [2014] Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) Girshick [2015] Girshick, R.: Fast R-CNN (2015) Ren et al. [2016] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. Computer Vision: A Reference Guide, 1–9 (2020) Contributors [2022] Contributors, M.: MMYOLO: OpenMMLab YOLO series toolbox and benchmark. https://github.com/open-mmlab/mmyolo (2022) Jocher et al. [2022] Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., Kwon, Y., Michael, K., Fang, J., Yifu, Z., Wong, C., Montes, D., et al.: ultralytics/yolov5: v7. 0-yolov5 sota realtime instance segmentation. Zenodo (2022) Jocher [2022] Jocher, G.: YOLOv5 v6.1 Release. https://community.ultralytics.com/t/yolov5-v6-1-release/51 (2022) Li et al. [2022] Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., Nie, W., Li, Y., Zhang, B., Liang, Y., Zhou, L., Xu, X., Chu, X., Wei, X., Wei, X.: YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications (2022) Li et al. [2023] Li, C., Li, L., Geng, Y., Jiang, H., Cheng, M., Zhang, B., Ke, Z., Xu, X., Chu, X.: YOLOv6 v3.0: A Full-Scale Reloading (2023) Wang et al. [2022] Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors (2022) Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics [32] What is YOLOv8? https://roboflow.com/model/yolov8 (2023) Aharon et al. [2021] Aharon, S., Louis-Dupont, Ofri Masad, Yurkova, K., Lotem Fridman, Lkdci, Khvedchenya, E., Rubin, R., Bagrov, N., Tymchenko, B., Keren, T., Zhilko, A., Eran-Deci: Super-Gradients. GitHub (2021). https://doi.org/10.5281/ZENODO.7789328 . https://zenodo.org/record/7789328 [34] YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016) Redmon and Farhadi [2018] Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018) Liu et al. [2016] Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21–37 (2016). Springer Girshick et al. [2014] Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) Girshick [2015] Girshick, R.: Fast R-CNN (2015) Ren et al. [2016] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. 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[2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. Computer Vision: A Reference Guide, 1–9 (2020) Contributors [2022] Contributors, M.: MMYOLO: OpenMMLab YOLO series toolbox and benchmark. https://github.com/open-mmlab/mmyolo (2022) Jocher et al. [2022] Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., Kwon, Y., Michael, K., Fang, J., Yifu, Z., Wong, C., Montes, D., et al.: ultralytics/yolov5: v7. 0-yolov5 sota realtime instance segmentation. Zenodo (2022) Jocher [2022] Jocher, G.: YOLOv5 v6.1 Release. https://community.ultralytics.com/t/yolov5-v6-1-release/51 (2022) Li et al. [2022] Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., Nie, W., Li, Y., Zhang, B., Liang, Y., Zhou, L., Xu, X., Chu, X., Wei, X., Wei, X.: YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications (2022) Li et al. [2023] Li, C., Li, L., Geng, Y., Jiang, H., Cheng, M., Zhang, B., Ke, Z., Xu, X., Chu, X.: YOLOv6 v3.0: A Full-Scale Reloading (2023) Wang et al. [2022] Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors (2022) Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics [32] What is YOLOv8? https://roboflow.com/model/yolov8 (2023) Aharon et al. [2021] Aharon, S., Louis-Dupont, Ofri Masad, Yurkova, K., Lotem Fridman, Lkdci, Khvedchenya, E., Rubin, R., Bagrov, N., Tymchenko, B., Keren, T., Zhilko, A., Eran-Deci: Super-Gradients. GitHub (2021). https://doi.org/10.5281/ZENODO.7789328 . https://zenodo.org/record/7789328 [34] YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Elsken, T., Metzen, J.H., Hutter, F.: Neural architecture search: A survey. The Journal of Machine Learning Research 20(1), 1997–2017 (2019) Said et al. [2023] Said, Y., Atri, M., Albahar, M.A., Ben Atitallah, A., Alsariera, Y.A.: Obstacle detection system for navigation assistance of visually impaired people based on deep learning techniques. Sensors 23(11), 5262 (2023) Rachburee and Punlumjeak [2021] Rachburee, N., Punlumjeak, W.: An assistive model of obstacle detection based on deep learning: Yolov3 for visually impaired people. International Journal of Electrical & Computer Engineering (2088-8708) 11(4) (2021) Yadav et al. [2020] Yadav, S., Joshi, R.C., Dutta, M.K., Kiac, M., Sikora, P.: Fusion of object recognition and obstacle detection approach for assisting visually challenged person. In: 2020 43rd International Conference on Telecommunications and Signal Processing (TSP), pp. 537–540 (2020). IEEE Redmon et al. [2016] Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016) Redmon and Farhadi [2018] Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018) Liu et al. [2016] Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21–37 (2016). Springer Girshick et al. [2014] Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) Girshick [2015] Girshick, R.: Fast R-CNN (2015) Ren et al. [2016] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. Computer Vision: A Reference Guide, 1–9 (2020) Contributors [2022] Contributors, M.: MMYOLO: OpenMMLab YOLO series toolbox and benchmark. https://github.com/open-mmlab/mmyolo (2022) Jocher et al. [2022] Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., Kwon, Y., Michael, K., Fang, J., Yifu, Z., Wong, C., Montes, D., et al.: ultralytics/yolov5: v7. 0-yolov5 sota realtime instance segmentation. Zenodo (2022) Jocher [2022] Jocher, G.: YOLOv5 v6.1 Release. https://community.ultralytics.com/t/yolov5-v6-1-release/51 (2022) Li et al. [2022] Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., Nie, W., Li, Y., Zhang, B., Liang, Y., Zhou, L., Xu, X., Chu, X., Wei, X., Wei, X.: YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications (2022) Li et al. [2023] Li, C., Li, L., Geng, Y., Jiang, H., Cheng, M., Zhang, B., Ke, Z., Xu, X., Chu, X.: YOLOv6 v3.0: A Full-Scale Reloading (2023) Wang et al. [2022] Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors (2022) Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics [32] What is YOLOv8? https://roboflow.com/model/yolov8 (2023) Aharon et al. [2021] Aharon, S., Louis-Dupont, Ofri Masad, Yurkova, K., Lotem Fridman, Lkdci, Khvedchenya, E., Rubin, R., Bagrov, N., Tymchenko, B., Keren, T., Zhilko, A., Eran-Deci: Super-Gradients. GitHub (2021). https://doi.org/10.5281/ZENODO.7789328 . https://zenodo.org/record/7789328 [34] YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Said, Y., Atri, M., Albahar, M.A., Ben Atitallah, A., Alsariera, Y.A.: Obstacle detection system for navigation assistance of visually impaired people based on deep learning techniques. Sensors 23(11), 5262 (2023) Rachburee and Punlumjeak [2021] Rachburee, N., Punlumjeak, W.: An assistive model of obstacle detection based on deep learning: Yolov3 for visually impaired people. International Journal of Electrical & Computer Engineering (2088-8708) 11(4) (2021) Yadav et al. [2020] Yadav, S., Joshi, R.C., Dutta, M.K., Kiac, M., Sikora, P.: Fusion of object recognition and obstacle detection approach for assisting visually challenged person. In: 2020 43rd International Conference on Telecommunications and Signal Processing (TSP), pp. 537–540 (2020). IEEE Redmon et al. [2016] Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016) Redmon and Farhadi [2018] Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018) Liu et al. [2016] Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21–37 (2016). Springer Girshick et al. [2014] Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) Girshick [2015] Girshick, R.: Fast R-CNN (2015) Ren et al. [2016] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. Computer Vision: A Reference Guide, 1–9 (2020) Contributors [2022] Contributors, M.: MMYOLO: OpenMMLab YOLO series toolbox and benchmark. https://github.com/open-mmlab/mmyolo (2022) Jocher et al. [2022] Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., Kwon, Y., Michael, K., Fang, J., Yifu, Z., Wong, C., Montes, D., et al.: ultralytics/yolov5: v7. 0-yolov5 sota realtime instance segmentation. Zenodo (2022) Jocher [2022] Jocher, G.: YOLOv5 v6.1 Release. https://community.ultralytics.com/t/yolov5-v6-1-release/51 (2022) Li et al. [2022] Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., Nie, W., Li, Y., Zhang, B., Liang, Y., Zhou, L., Xu, X., Chu, X., Wei, X., Wei, X.: YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications (2022) Li et al. [2023] Li, C., Li, L., Geng, Y., Jiang, H., Cheng, M., Zhang, B., Ke, Z., Xu, X., Chu, X.: YOLOv6 v3.0: A Full-Scale Reloading (2023) Wang et al. [2022] Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors (2022) Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics [32] What is YOLOv8? https://roboflow.com/model/yolov8 (2023) Aharon et al. [2021] Aharon, S., Louis-Dupont, Ofri Masad, Yurkova, K., Lotem Fridman, Lkdci, Khvedchenya, E., Rubin, R., Bagrov, N., Tymchenko, B., Keren, T., Zhilko, A., Eran-Deci: Super-Gradients. GitHub (2021). https://doi.org/10.5281/ZENODO.7789328 . https://zenodo.org/record/7789328 [34] YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Rachburee, N., Punlumjeak, W.: An assistive model of obstacle detection based on deep learning: Yolov3 for visually impaired people. International Journal of Electrical & Computer Engineering (2088-8708) 11(4) (2021) Yadav et al. [2020] Yadav, S., Joshi, R.C., Dutta, M.K., Kiac, M., Sikora, P.: Fusion of object recognition and obstacle detection approach for assisting visually challenged person. In: 2020 43rd International Conference on Telecommunications and Signal Processing (TSP), pp. 537–540 (2020). IEEE Redmon et al. [2016] Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016) Redmon and Farhadi [2018] Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018) Liu et al. [2016] Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21–37 (2016). Springer Girshick et al. [2014] Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) Girshick [2015] Girshick, R.: Fast R-CNN (2015) Ren et al. [2016] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. Computer Vision: A Reference Guide, 1–9 (2020) Contributors [2022] Contributors, M.: MMYOLO: OpenMMLab YOLO series toolbox and benchmark. https://github.com/open-mmlab/mmyolo (2022) Jocher et al. [2022] Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., Kwon, Y., Michael, K., Fang, J., Yifu, Z., Wong, C., Montes, D., et al.: ultralytics/yolov5: v7. 0-yolov5 sota realtime instance segmentation. Zenodo (2022) Jocher [2022] Jocher, G.: YOLOv5 v6.1 Release. https://community.ultralytics.com/t/yolov5-v6-1-release/51 (2022) Li et al. [2022] Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., Nie, W., Li, Y., Zhang, B., Liang, Y., Zhou, L., Xu, X., Chu, X., Wei, X., Wei, X.: YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications (2022) Li et al. [2023] Li, C., Li, L., Geng, Y., Jiang, H., Cheng, M., Zhang, B., Ke, Z., Xu, X., Chu, X.: YOLOv6 v3.0: A Full-Scale Reloading (2023) Wang et al. [2022] Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors (2022) Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics [32] What is YOLOv8? https://roboflow.com/model/yolov8 (2023) Aharon et al. [2021] Aharon, S., Louis-Dupont, Ofri Masad, Yurkova, K., Lotem Fridman, Lkdci, Khvedchenya, E., Rubin, R., Bagrov, N., Tymchenko, B., Keren, T., Zhilko, A., Eran-Deci: Super-Gradients. GitHub (2021). https://doi.org/10.5281/ZENODO.7789328 . https://zenodo.org/record/7789328 [34] YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Yadav, S., Joshi, R.C., Dutta, M.K., Kiac, M., Sikora, P.: Fusion of object recognition and obstacle detection approach for assisting visually challenged person. In: 2020 43rd International Conference on Telecommunications and Signal Processing (TSP), pp. 537–540 (2020). IEEE Redmon et al. [2016] Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016) Redmon and Farhadi [2018] Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018) Liu et al. [2016] Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21–37 (2016). Springer Girshick et al. [2014] Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) Girshick [2015] Girshick, R.: Fast R-CNN (2015) Ren et al. [2016] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. 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In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21–37 (2016). Springer Girshick et al. [2014] Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) Girshick [2015] Girshick, R.: Fast R-CNN (2015) Ren et al. [2016] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. Computer Vision: A Reference Guide, 1–9 (2020) Contributors [2022] Contributors, M.: MMYOLO: OpenMMLab YOLO series toolbox and benchmark. https://github.com/open-mmlab/mmyolo (2022) Jocher et al. [2022] Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., Kwon, Y., Michael, K., Fang, J., Yifu, Z., Wong, C., Montes, D., et al.: ultralytics/yolov5: v7. 0-yolov5 sota realtime instance segmentation. Zenodo (2022) Jocher [2022] Jocher, G.: YOLOv5 v6.1 Release. https://community.ultralytics.com/t/yolov5-v6-1-release/51 (2022) Li et al. [2022] Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., Nie, W., Li, Y., Zhang, B., Liang, Y., Zhou, L., Xu, X., Chu, X., Wei, X., Wei, X.: YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications (2022) Li et al. [2023] Li, C., Li, L., Geng, Y., Jiang, H., Cheng, M., Zhang, B., Ke, Z., Xu, X., Chu, X.: YOLOv6 v3.0: A Full-Scale Reloading (2023) Wang et al. [2022] Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors (2022) Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics [32] What is YOLOv8? https://roboflow.com/model/yolov8 (2023) Aharon et al. [2021] Aharon, S., Louis-Dupont, Ofri Masad, Yurkova, K., Lotem Fridman, Lkdci, Khvedchenya, E., Rubin, R., Bagrov, N., Tymchenko, B., Keren, T., Zhilko, A., Eran-Deci: Super-Gradients. GitHub (2021). https://doi.org/10.5281/ZENODO.7789328 . https://zenodo.org/record/7789328 [34] YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018) Liu et al. [2016] Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21–37 (2016). Springer Girshick et al. [2014] Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) Girshick [2015] Girshick, R.: Fast R-CNN (2015) Ren et al. [2016] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. Computer Vision: A Reference Guide, 1–9 (2020) Contributors [2022] Contributors, M.: MMYOLO: OpenMMLab YOLO series toolbox and benchmark. https://github.com/open-mmlab/mmyolo (2022) Jocher et al. [2022] Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., Kwon, Y., Michael, K., Fang, J., Yifu, Z., Wong, C., Montes, D., et al.: ultralytics/yolov5: v7. 0-yolov5 sota realtime instance segmentation. 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Algorithms 14(4), 114 (2021) Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21–37 (2016). Springer Girshick et al. [2014] Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) Girshick [2015] Girshick, R.: Fast R-CNN (2015) Ren et al. [2016] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. Computer Vision: A Reference Guide, 1–9 (2020) Contributors [2022] Contributors, M.: MMYOLO: OpenMMLab YOLO series toolbox and benchmark. https://github.com/open-mmlab/mmyolo (2022) Jocher et al. [2022] Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., Kwon, Y., Michael, K., Fang, J., Yifu, Z., Wong, C., Montes, D., et al.: ultralytics/yolov5: v7. 0-yolov5 sota realtime instance segmentation. Zenodo (2022) Jocher [2022] Jocher, G.: YOLOv5 v6.1 Release. https://community.ultralytics.com/t/yolov5-v6-1-release/51 (2022) Li et al. [2022] Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., Nie, W., Li, Y., Zhang, B., Liang, Y., Zhou, L., Xu, X., Chu, X., Wei, X., Wei, X.: YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications (2022) Li et al. [2023] Li, C., Li, L., Geng, Y., Jiang, H., Cheng, M., Zhang, B., Ke, Z., Xu, X., Chu, X.: YOLOv6 v3.0: A Full-Scale Reloading (2023) Wang et al. [2022] Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors (2022) Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics [32] What is YOLOv8? https://roboflow.com/model/yolov8 (2023) Aharon et al. [2021] Aharon, S., Louis-Dupont, Ofri Masad, Yurkova, K., Lotem Fridman, Lkdci, Khvedchenya, E., Rubin, R., Bagrov, N., Tymchenko, B., Keren, T., Zhilko, A., Eran-Deci: Super-Gradients. GitHub (2021). https://doi.org/10.5281/ZENODO.7789328 . https://zenodo.org/record/7789328 [34] YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) Girshick [2015] Girshick, R.: Fast R-CNN (2015) Ren et al. [2016] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. Computer Vision: A Reference Guide, 1–9 (2020) Contributors [2022] Contributors, M.: MMYOLO: OpenMMLab YOLO series toolbox and benchmark. https://github.com/open-mmlab/mmyolo (2022) Jocher et al. [2022] Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., Kwon, Y., Michael, K., Fang, J., Yifu, Z., Wong, C., Montes, D., et al.: ultralytics/yolov5: v7. 0-yolov5 sota realtime instance segmentation. Zenodo (2022) Jocher [2022] Jocher, G.: YOLOv5 v6.1 Release. https://community.ultralytics.com/t/yolov5-v6-1-release/51 (2022) Li et al. [2022] Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., Nie, W., Li, Y., Zhang, B., Liang, Y., Zhou, L., Xu, X., Chu, X., Wei, X., Wei, X.: YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications (2022) Li et al. [2023] Li, C., Li, L., Geng, Y., Jiang, H., Cheng, M., Zhang, B., Ke, Z., Xu, X., Chu, X.: YOLOv6 v3.0: A Full-Scale Reloading (2023) Wang et al. [2022] Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors (2022) Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics [32] What is YOLOv8? https://roboflow.com/model/yolov8 (2023) Aharon et al. [2021] Aharon, S., Louis-Dupont, Ofri Masad, Yurkova, K., Lotem Fridman, Lkdci, Khvedchenya, E., Rubin, R., Bagrov, N., Tymchenko, B., Keren, T., Zhilko, A., Eran-Deci: Super-Gradients. GitHub (2021). https://doi.org/10.5281/ZENODO.7789328 . https://zenodo.org/record/7789328 [34] YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. 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[2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. 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Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. Computer Vision: A Reference Guide, 1–9 (2020) Contributors [2022] Contributors, M.: MMYOLO: OpenMMLab YOLO series toolbox and benchmark. https://github.com/open-mmlab/mmyolo (2022) Jocher et al. [2022] Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., Kwon, Y., Michael, K., Fang, J., Yifu, Z., Wong, C., Montes, D., et al.: ultralytics/yolov5: v7. 0-yolov5 sota realtime instance segmentation. Zenodo (2022) Jocher [2022] Jocher, G.: YOLOv5 v6.1 Release. https://community.ultralytics.com/t/yolov5-v6-1-release/51 (2022) Li et al. [2022] Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., Nie, W., Li, Y., Zhang, B., Liang, Y., Zhou, L., Xu, X., Chu, X., Wei, X., Wei, X.: YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications (2022) Li et al. [2023] Li, C., Li, L., Geng, Y., Jiang, H., Cheng, M., Zhang, B., Ke, Z., Xu, X., Chu, X.: YOLOv6 v3.0: A Full-Scale Reloading (2023) Wang et al. [2022] Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors (2022) Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics [32] What is YOLOv8? https://roboflow.com/model/yolov8 (2023) Aharon et al. [2021] Aharon, S., Louis-Dupont, Ofri Masad, Yurkova, K., Lotem Fridman, Lkdci, Khvedchenya, E., Rubin, R., Bagrov, N., Tymchenko, B., Keren, T., Zhilko, A., Eran-Deci: Super-Gradients. GitHub (2021). https://doi.org/10.5281/ZENODO.7789328 . https://zenodo.org/record/7789328 [34] YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. 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International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. Computer Vision: A Reference Guide, 1–9 (2020) Contributors [2022] Contributors, M.: MMYOLO: OpenMMLab YOLO series toolbox and benchmark. https://github.com/open-mmlab/mmyolo (2022) Jocher et al. [2022] Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., Kwon, Y., Michael, K., Fang, J., Yifu, Z., Wong, C., Montes, D., et al.: ultralytics/yolov5: v7. 0-yolov5 sota realtime instance segmentation. Zenodo (2022) Jocher [2022] Jocher, G.: YOLOv5 v6.1 Release. https://community.ultralytics.com/t/yolov5-v6-1-release/51 (2022) Li et al. 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[2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. 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[2022] Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors (2022) Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics [32] What is YOLOv8? https://roboflow.com/model/yolov8 (2023) Aharon et al. [2021] Aharon, S., Louis-Dupont, Ofri Masad, Yurkova, K., Lotem Fridman, Lkdci, Khvedchenya, E., Rubin, R., Bagrov, N., Tymchenko, B., Keren, T., Zhilko, A., Eran-Deci: Super-Gradients. GitHub (2021). https://doi.org/10.5281/ZENODO.7789328 . https://zenodo.org/record/7789328 [34] YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. Computer Vision: A Reference Guide, 1–9 (2020) Contributors [2022] Contributors, M.: MMYOLO: OpenMMLab YOLO series toolbox and benchmark. https://github.com/open-mmlab/mmyolo (2022) Jocher et al. [2022] Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., Kwon, Y., Michael, K., Fang, J., Yifu, Z., Wong, C., Montes, D., et al.: ultralytics/yolov5: v7. 0-yolov5 sota realtime instance segmentation. Zenodo (2022) Jocher [2022] Jocher, G.: YOLOv5 v6.1 Release. https://community.ultralytics.com/t/yolov5-v6-1-release/51 (2022) Li et al. [2022] Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., Nie, W., Li, Y., Zhang, B., Liang, Y., Zhou, L., Xu, X., Chu, X., Wei, X., Wei, X.: YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications (2022) Li et al. [2023] Li, C., Li, L., Geng, Y., Jiang, H., Cheng, M., Zhang, B., Ke, Z., Xu, X., Chu, X.: YOLOv6 v3.0: A Full-Scale Reloading (2023) Wang et al. [2022] Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors (2022) Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics [32] What is YOLOv8? https://roboflow.com/model/yolov8 (2023) Aharon et al. [2021] Aharon, S., Louis-Dupont, Ofri Masad, Yurkova, K., Lotem Fridman, Lkdci, Khvedchenya, E., Rubin, R., Bagrov, N., Tymchenko, B., Keren, T., Zhilko, A., Eran-Deci: Super-Gradients. GitHub (2021). https://doi.org/10.5281/ZENODO.7789328 . https://zenodo.org/record/7789328 [34] YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. Computer Vision: A Reference Guide, 1–9 (2020) Contributors [2022] Contributors, M.: MMYOLO: OpenMMLab YOLO series toolbox and benchmark. https://github.com/open-mmlab/mmyolo (2022) Jocher et al. [2022] Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., Kwon, Y., Michael, K., Fang, J., Yifu, Z., Wong, C., Montes, D., et al.: ultralytics/yolov5: v7. 0-yolov5 sota realtime instance segmentation. Zenodo (2022) Jocher [2022] Jocher, G.: YOLOv5 v6.1 Release. https://community.ultralytics.com/t/yolov5-v6-1-release/51 (2022) Li et al. [2022] Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., Nie, W., Li, Y., Zhang, B., Liang, Y., Zhou, L., Xu, X., Chu, X., Wei, X., Wei, X.: YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications (2022) Li et al. [2023] Li, C., Li, L., Geng, Y., Jiang, H., Cheng, M., Zhang, B., Ke, Z., Xu, X., Chu, X.: YOLOv6 v3.0: A Full-Scale Reloading (2023) Wang et al. [2022] Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors (2022) Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics [32] What is YOLOv8? https://roboflow.com/model/yolov8 (2023) Aharon et al. 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[2016] Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21–37 (2016). Springer Girshick et al. [2014] Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) Girshick [2015] Girshick, R.: Fast R-CNN (2015) Ren et al. [2016] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. 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[2022] Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors (2022) Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics [32] What is YOLOv8? https://roboflow.com/model/yolov8 (2023) Aharon et al. [2021] Aharon, S., Louis-Dupont, Ofri Masad, Yurkova, K., Lotem Fridman, Lkdci, Khvedchenya, E., Rubin, R., Bagrov, N., Tymchenko, B., Keren, T., Zhilko, A., Eran-Deci: Super-Gradients. GitHub (2021). https://doi.org/10.5281/ZENODO.7789328 . https://zenodo.org/record/7789328 [34] YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Rachburee, N., Punlumjeak, W.: An assistive model of obstacle detection based on deep learning: Yolov3 for visually impaired people. International Journal of Electrical & Computer Engineering (2088-8708) 11(4) (2021) Yadav et al. [2020] Yadav, S., Joshi, R.C., Dutta, M.K., Kiac, M., Sikora, P.: Fusion of object recognition and obstacle detection approach for assisting visually challenged person. In: 2020 43rd International Conference on Telecommunications and Signal Processing (TSP), pp. 537–540 (2020). IEEE Redmon et al. [2016] Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016) Redmon and Farhadi [2018] Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018) Liu et al. [2016] Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21–37 (2016). Springer Girshick et al. [2014] Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) Girshick [2015] Girshick, R.: Fast R-CNN (2015) Ren et al. 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International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. Computer Vision: A Reference Guide, 1–9 (2020) Contributors [2022] Contributors, M.: MMYOLO: OpenMMLab YOLO series toolbox and benchmark. https://github.com/open-mmlab/mmyolo (2022) Jocher et al. [2022] Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., Kwon, Y., Michael, K., Fang, J., Yifu, Z., Wong, C., Montes, D., et al.: ultralytics/yolov5: v7. 0-yolov5 sota realtime instance segmentation. Zenodo (2022) Jocher [2022] Jocher, G.: YOLOv5 v6.1 Release. https://community.ultralytics.com/t/yolov5-v6-1-release/51 (2022) Li et al. [2022] Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., Nie, W., Li, Y., Zhang, B., Liang, Y., Zhou, L., Xu, X., Chu, X., Wei, X., Wei, X.: YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications (2022) Li et al. [2023] Li, C., Li, L., Geng, Y., Jiang, H., Cheng, M., Zhang, B., Ke, Z., Xu, X., Chu, X.: YOLOv6 v3.0: A Full-Scale Reloading (2023) Wang et al. [2022] Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors (2022) Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics [32] What is YOLOv8? https://roboflow.com/model/yolov8 (2023) Aharon et al. [2021] Aharon, S., Louis-Dupont, Ofri Masad, Yurkova, K., Lotem Fridman, Lkdci, Khvedchenya, E., Rubin, R., Bagrov, N., Tymchenko, B., Keren, T., Zhilko, A., Eran-Deci: Super-Gradients. GitHub (2021). https://doi.org/10.5281/ZENODO.7789328 . https://zenodo.org/record/7789328 [34] YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Yadav, S., Joshi, R.C., Dutta, M.K., Kiac, M., Sikora, P.: Fusion of object recognition and obstacle detection approach for assisting visually challenged person. 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In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) Girshick [2015] Girshick, R.: Fast R-CNN (2015) Ren et al. [2016] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. Computer Vision: A Reference Guide, 1–9 (2020) Contributors [2022] Contributors, M.: MMYOLO: OpenMMLab YOLO series toolbox and benchmark. https://github.com/open-mmlab/mmyolo (2022) Jocher et al. [2022] Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., Kwon, Y., Michael, K., Fang, J., Yifu, Z., Wong, C., Montes, D., et al.: ultralytics/yolov5: v7. 0-yolov5 sota realtime instance segmentation. 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Algorithms 14(4), 114 (2021) Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016) Redmon and Farhadi [2018] Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018) Liu et al. [2016] Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21–37 (2016). Springer Girshick et al. [2014] Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) Girshick [2015] Girshick, R.: Fast R-CNN (2015) Ren et al. [2016] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. Computer Vision: A Reference Guide, 1–9 (2020) Contributors [2022] Contributors, M.: MMYOLO: OpenMMLab YOLO series toolbox and benchmark. https://github.com/open-mmlab/mmyolo (2022) Jocher et al. [2022] Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., Kwon, Y., Michael, K., Fang, J., Yifu, Z., Wong, C., Montes, D., et al.: ultralytics/yolov5: v7. 0-yolov5 sota realtime instance segmentation. Zenodo (2022) Jocher [2022] Jocher, G.: YOLOv5 v6.1 Release. https://community.ultralytics.com/t/yolov5-v6-1-release/51 (2022) Li et al. [2022] Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., Nie, W., Li, Y., Zhang, B., Liang, Y., Zhou, L., Xu, X., Chu, X., Wei, X., Wei, X.: YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications (2022) Li et al. [2023] Li, C., Li, L., Geng, Y., Jiang, H., Cheng, M., Zhang, B., Ke, Z., Xu, X., Chu, X.: YOLOv6 v3.0: A Full-Scale Reloading (2023) Wang et al. [2022] Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors (2022) Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics [32] What is YOLOv8? https://roboflow.com/model/yolov8 (2023) Aharon et al. [2021] Aharon, S., Louis-Dupont, Ofri Masad, Yurkova, K., Lotem Fridman, Lkdci, Khvedchenya, E., Rubin, R., Bagrov, N., Tymchenko, B., Keren, T., Zhilko, A., Eran-Deci: Super-Gradients. 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In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21–37 (2016). Springer Girshick et al. [2014] Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) Girshick [2015] Girshick, R.: Fast R-CNN (2015) Ren et al. [2016] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. Computer Vision: A Reference Guide, 1–9 (2020) Contributors [2022] Contributors, M.: MMYOLO: OpenMMLab YOLO series toolbox and benchmark. https://github.com/open-mmlab/mmyolo (2022) Jocher et al. [2022] Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., Kwon, Y., Michael, K., Fang, J., Yifu, Z., Wong, C., Montes, D., et al.: ultralytics/yolov5: v7. 0-yolov5 sota realtime instance segmentation. Zenodo (2022) Jocher [2022] Jocher, G.: YOLOv5 v6.1 Release. https://community.ultralytics.com/t/yolov5-v6-1-release/51 (2022) Li et al. [2022] Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., Nie, W., Li, Y., Zhang, B., Liang, Y., Zhou, L., Xu, X., Chu, X., Wei, X., Wei, X.: YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications (2022) Li et al. [2023] Li, C., Li, L., Geng, Y., Jiang, H., Cheng, M., Zhang, B., Ke, Z., Xu, X., Chu, X.: YOLOv6 v3.0: A Full-Scale Reloading (2023) Wang et al. [2022] Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors (2022) Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics [32] What is YOLOv8? https://roboflow.com/model/yolov8 (2023) Aharon et al. [2021] Aharon, S., Louis-Dupont, Ofri Masad, Yurkova, K., Lotem Fridman, Lkdci, Khvedchenya, E., Rubin, R., Bagrov, N., Tymchenko, B., Keren, T., Zhilko, A., Eran-Deci: Super-Gradients. GitHub (2021). https://doi.org/10.5281/ZENODO.7789328 . https://zenodo.org/record/7789328 [34] YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21–37 (2016). Springer Girshick et al. [2014] Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) Girshick [2015] Girshick, R.: Fast R-CNN (2015) Ren et al. [2016] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. Computer Vision: A Reference Guide, 1–9 (2020) Contributors [2022] Contributors, M.: MMYOLO: OpenMMLab YOLO series toolbox and benchmark. https://github.com/open-mmlab/mmyolo (2022) Jocher et al. [2022] Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., Kwon, Y., Michael, K., Fang, J., Yifu, Z., Wong, C., Montes, D., et al.: ultralytics/yolov5: v7. 0-yolov5 sota realtime instance segmentation. Zenodo (2022) Jocher [2022] Jocher, G.: YOLOv5 v6.1 Release. https://community.ultralytics.com/t/yolov5-v6-1-release/51 (2022) Li et al. [2022] Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., Nie, W., Li, Y., Zhang, B., Liang, Y., Zhou, L., Xu, X., Chu, X., Wei, X., Wei, X.: YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications (2022) Li et al. [2023] Li, C., Li, L., Geng, Y., Jiang, H., Cheng, M., Zhang, B., Ke, Z., Xu, X., Chu, X.: YOLOv6 v3.0: A Full-Scale Reloading (2023) Wang et al. [2022] Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors (2022) Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics [32] What is YOLOv8? https://roboflow.com/model/yolov8 (2023) Aharon et al. [2021] Aharon, S., Louis-Dupont, Ofri Masad, Yurkova, K., Lotem Fridman, Lkdci, Khvedchenya, E., Rubin, R., Bagrov, N., Tymchenko, B., Keren, T., Zhilko, A., Eran-Deci: Super-Gradients. GitHub (2021). https://doi.org/10.5281/ZENODO.7789328 . https://zenodo.org/record/7789328 [34] YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) Girshick [2015] Girshick, R.: Fast R-CNN (2015) Ren et al. [2016] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. Computer Vision: A Reference Guide, 1–9 (2020) Contributors [2022] Contributors, M.: MMYOLO: OpenMMLab YOLO series toolbox and benchmark. https://github.com/open-mmlab/mmyolo (2022) Jocher et al. [2022] Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., Kwon, Y., Michael, K., Fang, J., Yifu, Z., Wong, C., Montes, D., et al.: ultralytics/yolov5: v7. 0-yolov5 sota realtime instance segmentation. Zenodo (2022) Jocher [2022] Jocher, G.: YOLOv5 v6.1 Release. https://community.ultralytics.com/t/yolov5-v6-1-release/51 (2022) Li et al. [2022] Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., Nie, W., Li, Y., Zhang, B., Liang, Y., Zhou, L., Xu, X., Chu, X., Wei, X., Wei, X.: YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications (2022) Li et al. [2023] Li, C., Li, L., Geng, Y., Jiang, H., Cheng, M., Zhang, B., Ke, Z., Xu, X., Chu, X.: YOLOv6 v3.0: A Full-Scale Reloading (2023) Wang et al. [2022] Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors (2022) Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics [32] What is YOLOv8? https://roboflow.com/model/yolov8 (2023) Aharon et al. [2021] Aharon, S., Louis-Dupont, Ofri Masad, Yurkova, K., Lotem Fridman, Lkdci, Khvedchenya, E., Rubin, R., Bagrov, N., Tymchenko, B., Keren, T., Zhilko, A., Eran-Deci: Super-Gradients. GitHub (2021). https://doi.org/10.5281/ZENODO.7789328 . https://zenodo.org/record/7789328 [34] YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Girshick, R.: Fast R-CNN (2015) Ren et al. [2016] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. 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[2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. 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In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016) Redmon and Farhadi [2018] Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018) Liu et al. [2016] Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21–37 (2016). Springer Girshick et al. [2014] Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) Girshick [2015] Girshick, R.: Fast R-CNN (2015) Ren et al. [2016] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. Computer Vision: A Reference Guide, 1–9 (2020) Contributors [2022] Contributors, M.: MMYOLO: OpenMMLab YOLO series toolbox and benchmark. https://github.com/open-mmlab/mmyolo (2022) Jocher et al. [2022] Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., Kwon, Y., Michael, K., Fang, J., Yifu, Z., Wong, C., Montes, D., et al.: ultralytics/yolov5: v7. 0-yolov5 sota realtime instance segmentation. Zenodo (2022) Jocher [2022] Jocher, G.: YOLOv5 v6.1 Release. https://community.ultralytics.com/t/yolov5-v6-1-release/51 (2022) Li et al. [2022] Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., Nie, W., Li, Y., Zhang, B., Liang, Y., Zhou, L., Xu, X., Chu, X., Wei, X., Wei, X.: YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications (2022) Li et al. [2023] Li, C., Li, L., Geng, Y., Jiang, H., Cheng, M., Zhang, B., Ke, Z., Xu, X., Chu, X.: YOLOv6 v3.0: A Full-Scale Reloading (2023) Wang et al. [2022] Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors (2022) Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics [32] What is YOLOv8? https://roboflow.com/model/yolov8 (2023) Aharon et al. [2021] Aharon, S., Louis-Dupont, Ofri Masad, Yurkova, K., Lotem Fridman, Lkdci, Khvedchenya, E., Rubin, R., Bagrov, N., Tymchenko, B., Keren, T., Zhilko, A., Eran-Deci: Super-Gradients. GitHub (2021). https://doi.org/10.5281/ZENODO.7789328 . https://zenodo.org/record/7789328 [34] YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Rachburee, N., Punlumjeak, W.: An assistive model of obstacle detection based on deep learning: Yolov3 for visually impaired people. International Journal of Electrical & Computer Engineering (2088-8708) 11(4) (2021) Yadav et al. [2020] Yadav, S., Joshi, R.C., Dutta, M.K., Kiac, M., Sikora, P.: Fusion of object recognition and obstacle detection approach for assisting visually challenged person. In: 2020 43rd International Conference on Telecommunications and Signal Processing (TSP), pp. 537–540 (2020). IEEE Redmon et al. [2016] Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016) Redmon and Farhadi [2018] Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018) Liu et al. [2016] Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21–37 (2016). Springer Girshick et al. [2014] Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) Girshick [2015] Girshick, R.: Fast R-CNN (2015) Ren et al. [2016] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. Computer Vision: A Reference Guide, 1–9 (2020) Contributors [2022] Contributors, M.: MMYOLO: OpenMMLab YOLO series toolbox and benchmark. https://github.com/open-mmlab/mmyolo (2022) Jocher et al. [2022] Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., Kwon, Y., Michael, K., Fang, J., Yifu, Z., Wong, C., Montes, D., et al.: ultralytics/yolov5: v7. 0-yolov5 sota realtime instance segmentation. Zenodo (2022) Jocher [2022] Jocher, G.: YOLOv5 v6.1 Release. https://community.ultralytics.com/t/yolov5-v6-1-release/51 (2022) Li et al. [2022] Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., Nie, W., Li, Y., Zhang, B., Liang, Y., Zhou, L., Xu, X., Chu, X., Wei, X., Wei, X.: YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications (2022) Li et al. [2023] Li, C., Li, L., Geng, Y., Jiang, H., Cheng, M., Zhang, B., Ke, Z., Xu, X., Chu, X.: YOLOv6 v3.0: A Full-Scale Reloading (2023) Wang et al. [2022] Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors (2022) Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics [32] What is YOLOv8? https://roboflow.com/model/yolov8 (2023) Aharon et al. [2021] Aharon, S., Louis-Dupont, Ofri Masad, Yurkova, K., Lotem Fridman, Lkdci, Khvedchenya, E., Rubin, R., Bagrov, N., Tymchenko, B., Keren, T., Zhilko, A., Eran-Deci: Super-Gradients. GitHub (2021). https://doi.org/10.5281/ZENODO.7789328 . https://zenodo.org/record/7789328 [34] YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Yadav, S., Joshi, R.C., Dutta, M.K., Kiac, M., Sikora, P.: Fusion of object recognition and obstacle detection approach for assisting visually challenged person. In: 2020 43rd International Conference on Telecommunications and Signal Processing (TSP), pp. 537–540 (2020). IEEE Redmon et al. [2016] Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016) Redmon and Farhadi [2018] Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018) Liu et al. [2016] Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21–37 (2016). Springer Girshick et al. [2014] Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) Girshick [2015] Girshick, R.: Fast R-CNN (2015) Ren et al. [2016] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. Computer Vision: A Reference Guide, 1–9 (2020) Contributors [2022] Contributors, M.: MMYOLO: OpenMMLab YOLO series toolbox and benchmark. https://github.com/open-mmlab/mmyolo (2022) Jocher et al. [2022] Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., Kwon, Y., Michael, K., Fang, J., Yifu, Z., Wong, C., Montes, D., et al.: ultralytics/yolov5: v7. 0-yolov5 sota realtime instance segmentation. Zenodo (2022) Jocher [2022] Jocher, G.: YOLOv5 v6.1 Release. https://community.ultralytics.com/t/yolov5-v6-1-release/51 (2022) Li et al. [2022] Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., Nie, W., Li, Y., Zhang, B., Liang, Y., Zhou, L., Xu, X., Chu, X., Wei, X., Wei, X.: YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications (2022) Li et al. [2023] Li, C., Li, L., Geng, Y., Jiang, H., Cheng, M., Zhang, B., Ke, Z., Xu, X., Chu, X.: YOLOv6 v3.0: A Full-Scale Reloading (2023) Wang et al. [2022] Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors (2022) Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics [32] What is YOLOv8? https://roboflow.com/model/yolov8 (2023) Aharon et al. [2021] Aharon, S., Louis-Dupont, Ofri Masad, Yurkova, K., Lotem Fridman, Lkdci, Khvedchenya, E., Rubin, R., Bagrov, N., Tymchenko, B., Keren, T., Zhilko, A., Eran-Deci: Super-Gradients. GitHub (2021). https://doi.org/10.5281/ZENODO.7789328 . https://zenodo.org/record/7789328 [34] YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016) Redmon and Farhadi [2018] Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018) Liu et al. [2016] Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21–37 (2016). Springer Girshick et al. [2014] Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) Girshick [2015] Girshick, R.: Fast R-CNN (2015) Ren et al. [2016] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. 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In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) Girshick [2015] Girshick, R.: Fast R-CNN (2015) Ren et al. [2016] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. 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Algorithms 14(4), 114 (2021) Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21–37 (2016). Springer Girshick et al. [2014] Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) Girshick [2015] Girshick, R.: Fast R-CNN (2015) Ren et al. [2016] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. 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IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. Computer Vision: A Reference Guide, 1–9 (2020) Contributors [2022] Contributors, M.: MMYOLO: OpenMMLab YOLO series toolbox and benchmark. https://github.com/open-mmlab/mmyolo (2022) Jocher et al. [2022] Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., Kwon, Y., Michael, K., Fang, J., Yifu, Z., Wong, C., Montes, D., et al.: ultralytics/yolov5: v7. 0-yolov5 sota realtime instance segmentation. Zenodo (2022) Jocher [2022] Jocher, G.: YOLOv5 v6.1 Release. https://community.ultralytics.com/t/yolov5-v6-1-release/51 (2022) Li et al. [2022] Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., Nie, W., Li, Y., Zhang, B., Liang, Y., Zhou, L., Xu, X., Chu, X., Wei, X., Wei, X.: YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications (2022) Li et al. 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Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. 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[2022] Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors (2022) Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics [32] What is YOLOv8? https://roboflow.com/model/yolov8 (2023) Aharon et al. [2021] Aharon, S., Louis-Dupont, Ofri Masad, Yurkova, K., Lotem Fridman, Lkdci, Khvedchenya, E., Rubin, R., Bagrov, N., Tymchenko, B., Keren, T., Zhilko, A., Eran-Deci: Super-Gradients. GitHub (2021). https://doi.org/10.5281/ZENODO.7789328 . https://zenodo.org/record/7789328 [34] YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Yadav, S., Joshi, R.C., Dutta, M.K., Kiac, M., Sikora, P.: Fusion of object recognition and obstacle detection approach for assisting visually challenged person. In: 2020 43rd International Conference on Telecommunications and Signal Processing (TSP), pp. 537–540 (2020). IEEE Redmon et al. [2016] Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016) Redmon and Farhadi [2018] Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018) Liu et al. [2016] Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21–37 (2016). Springer Girshick et al. [2014] Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) Girshick [2015] Girshick, R.: Fast R-CNN (2015) Ren et al. [2016] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. Computer Vision: A Reference Guide, 1–9 (2020) Contributors [2022] Contributors, M.: MMYOLO: OpenMMLab YOLO series toolbox and benchmark. https://github.com/open-mmlab/mmyolo (2022) Jocher et al. [2022] Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., Kwon, Y., Michael, K., Fang, J., Yifu, Z., Wong, C., Montes, D., et al.: ultralytics/yolov5: v7. 0-yolov5 sota realtime instance segmentation. Zenodo (2022) Jocher [2022] Jocher, G.: YOLOv5 v6.1 Release. https://community.ultralytics.com/t/yolov5-v6-1-release/51 (2022) Li et al. [2022] Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., Nie, W., Li, Y., Zhang, B., Liang, Y., Zhou, L., Xu, X., Chu, X., Wei, X., Wei, X.: YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications (2022) Li et al. [2023] Li, C., Li, L., Geng, Y., Jiang, H., Cheng, M., Zhang, B., Ke, Z., Xu, X., Chu, X.: YOLOv6 v3.0: A Full-Scale Reloading (2023) Wang et al. [2022] Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors (2022) Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics [32] What is YOLOv8? https://roboflow.com/model/yolov8 (2023) Aharon et al. [2021] Aharon, S., Louis-Dupont, Ofri Masad, Yurkova, K., Lotem Fridman, Lkdci, Khvedchenya, E., Rubin, R., Bagrov, N., Tymchenko, B., Keren, T., Zhilko, A., Eran-Deci: Super-Gradients. GitHub (2021). https://doi.org/10.5281/ZENODO.7789328 . https://zenodo.org/record/7789328 [34] YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016) Redmon and Farhadi [2018] Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018) Liu et al. [2016] Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21–37 (2016). Springer Girshick et al. [2014] Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) Girshick [2015] Girshick, R.: Fast R-CNN (2015) Ren et al. [2016] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. Computer Vision: A Reference Guide, 1–9 (2020) Contributors [2022] Contributors, M.: MMYOLO: OpenMMLab YOLO series toolbox and benchmark. https://github.com/open-mmlab/mmyolo (2022) Jocher et al. [2022] Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., Kwon, Y., Michael, K., Fang, J., Yifu, Z., Wong, C., Montes, D., et al.: ultralytics/yolov5: v7. 0-yolov5 sota realtime instance segmentation. Zenodo (2022) Jocher [2022] Jocher, G.: YOLOv5 v6.1 Release. https://community.ultralytics.com/t/yolov5-v6-1-release/51 (2022) Li et al. 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GitHub (2021). https://doi.org/10.5281/ZENODO.7789328 . https://zenodo.org/record/7789328 [34] YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018) Liu et al. [2016] Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21–37 (2016). Springer Girshick et al. [2014] Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) Girshick [2015] Girshick, R.: Fast R-CNN (2015) Ren et al. [2016] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. Computer Vision: A Reference Guide, 1–9 (2020) Contributors [2022] Contributors, M.: MMYOLO: OpenMMLab YOLO series toolbox and benchmark. https://github.com/open-mmlab/mmyolo (2022) Jocher et al. [2022] Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., Kwon, Y., Michael, K., Fang, J., Yifu, Z., Wong, C., Montes, D., et al.: ultralytics/yolov5: v7. 0-yolov5 sota realtime instance segmentation. Zenodo (2022) Jocher [2022] Jocher, G.: YOLOv5 v6.1 Release. https://community.ultralytics.com/t/yolov5-v6-1-release/51 (2022) Li et al. [2022] Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., Nie, W., Li, Y., Zhang, B., Liang, Y., Zhou, L., Xu, X., Chu, X., Wei, X., Wei, X.: YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications (2022) Li et al. [2023] Li, C., Li, L., Geng, Y., Jiang, H., Cheng, M., Zhang, B., Ke, Z., Xu, X., Chu, X.: YOLOv6 v3.0: A Full-Scale Reloading (2023) Wang et al. [2022] Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors (2022) Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics [32] What is YOLOv8? https://roboflow.com/model/yolov8 (2023) Aharon et al. [2021] Aharon, S., Louis-Dupont, Ofri Masad, Yurkova, K., Lotem Fridman, Lkdci, Khvedchenya, E., Rubin, R., Bagrov, N., Tymchenko, B., Keren, T., Zhilko, A., Eran-Deci: Super-Gradients. GitHub (2021). https://doi.org/10.5281/ZENODO.7789328 . https://zenodo.org/record/7789328 [34] YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21–37 (2016). Springer Girshick et al. [2014] Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) Girshick [2015] Girshick, R.: Fast R-CNN (2015) Ren et al. [2016] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. 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[2016] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. 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Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2016) He et al. [2018] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018) Yamashita et al. [2018] Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 611–629 (2018) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. 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[2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics [32] What is YOLOv8? https://roboflow.com/model/yolov8 (2023) Aharon et al. [2021] Aharon, S., Louis-Dupont, Ofri Masad, Yurkova, K., Lotem Fridman, Lkdci, Khvedchenya, E., Rubin, R., Bagrov, N., Tymchenko, B., Keren, T., Zhilko, A., Eran-Deci: Super-Gradients. GitHub (2021). https://doi.org/10.5281/ZENODO.7789328 . https://zenodo.org/record/7789328 [34] YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. 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International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. Computer Vision: A Reference Guide, 1–9 (2020) Contributors [2022] Contributors, M.: MMYOLO: OpenMMLab YOLO series toolbox and benchmark. https://github.com/open-mmlab/mmyolo (2022) Jocher et al. [2022] Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., Kwon, Y., Michael, K., Fang, J., Yifu, Z., Wong, C., Montes, D., et al.: ultralytics/yolov5: v7. 0-yolov5 sota realtime instance segmentation. Zenodo (2022) Jocher [2022] Jocher, G.: YOLOv5 v6.1 Release. https://community.ultralytics.com/t/yolov5-v6-1-release/51 (2022) Li et al. 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[2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. 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Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Li et al. [2021] Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. [2019] Vahab, A., Naik, M.S., Raikar, P.G., Prasad, S.: Applications of object detection system. International Research Journal of Engineering and Technology (IRJET) 6(4), 4186–4192 (2019) Masud et al. [2022] Masud, U., Saeed, T., Malaikah, H.M., Islam, F.U., Abbas, G.: Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access 10, 13428–13441 (2022) Amit et al. [2020] Amit, Y., Felzenszwalb, P., Girshick, R.: Object detection. Computer Vision: A Reference Guide, 1–9 (2020) Contributors [2022] Contributors, M.: MMYOLO: OpenMMLab YOLO series toolbox and benchmark. https://github.com/open-mmlab/mmyolo (2022) Jocher et al. [2022] Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., Kwon, Y., Michael, K., Fang, J., Yifu, Z., Wong, C., Montes, D., et al.: ultralytics/yolov5: v7. 0-yolov5 sota realtime instance segmentation. Zenodo (2022) Jocher [2022] Jocher, G.: YOLOv5 v6.1 Release. https://community.ultralytics.com/t/yolov5-v6-1-release/51 (2022) Li et al. 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GitHub (2021). https://doi.org/10.5281/ZENODO.7789328 . https://zenodo.org/record/7789328 [34] YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021) Vahab et al. 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  29. Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors (2022) Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics [32] What is YOLOv8? https://roboflow.com/model/yolov8 (2023) Aharon et al. [2021] Aharon, S., Louis-Dupont, Ofri Masad, Yurkova, K., Lotem Fridman, Lkdci, Khvedchenya, E., Rubin, R., Bagrov, N., Tymchenko, B., Keren, T., Zhilko, A., Eran-Deci: Super-Gradients. GitHub (2021). https://doi.org/10.5281/ZENODO.7789328 . https://zenodo.org/record/7789328 [34] YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. 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  31. What is YOLOv8? https://roboflow.com/model/yolov8 (2023) Aharon et al. [2021] Aharon, S., Louis-Dupont, Ofri Masad, Yurkova, K., Lotem Fridman, Lkdci, Khvedchenya, E., Rubin, R., Bagrov, N., Tymchenko, B., Keren, T., Zhilko, A., Eran-Deci: Super-Gradients. GitHub (2021). https://doi.org/10.5281/ZENODO.7789328 . https://zenodo.org/record/7789328 [34] YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Aharon, S., Louis-Dupont, Ofri Masad, Yurkova, K., Lotem Fridman, Lkdci, Khvedchenya, E., Rubin, R., Bagrov, N., Tymchenko, B., Keren, T., Zhilko, A., Eran-Deci: Super-Gradients. GitHub (2021). https://doi.org/10.5281/ZENODO.7789328 . https://zenodo.org/record/7789328 [34] YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021)
  32. Aharon, S., Louis-Dupont, Ofri Masad, Yurkova, K., Lotem Fridman, Lkdci, Khvedchenya, E., Rubin, R., Bagrov, N., Tymchenko, B., Keren, T., Zhilko, A., Eran-Deci: Super-Gradients. GitHub (2021). https://doi.org/10.5281/ZENODO.7789328 . https://zenodo.org/record/7789328 [34] YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021)
  33. YOLO-NAS Sets a New Standard for Object Detection. https://analyticsindiamag.com/yolo-nas-sets-a-new-standard-for-object-detection/ (2023) Tang et al. [2023] Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021)
  34. Tang, W., Liu, D.-e., Zhao, X., Chen, Z., Zhao, C.: A dataset for the recognition of obstacles on blind sidewalk. Universal Access in the Information Society 22(1), 69–82 (2023) [36] Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Kaggle. https://www.kaggle.com/ [37] Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Google Colab. https://colab.research.google.com/ Kasper-Eulaers et al. [2021] Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021) Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021)
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  37. Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø., Kummervold, P.E.: Detecting heavy goods vehicles in rest areas in winter conditions using yolov5. Algorithms 14(4), 114 (2021)
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  1. Chenhao He (1 paper)
  2. Pramit Saha (28 papers)
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