Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
117 tokens/sec
GPT-4o
8 tokens/sec
Gemini 2.5 Pro Pro
47 tokens/sec
o3 Pro
5 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

YOLO-Ant: A Lightweight Detector via Depthwise Separable Convolutional and Large Kernel Design for Antenna Interference Source Detection (2402.12641v1)

Published 20 Feb 2024 in cs.CV

Abstract: In the era of 5G communication, removing interference sources that affect communication is a resource-intensive task. The rapid development of computer vision has enabled unmanned aerial vehicles to perform various high-altitude detection tasks. Because the field of object detection for antenna interference sources has not been fully explored, this industry lacks dedicated learning samples and detection models for this specific task. In this article, an antenna dataset is created to address important antenna interference source detection issues and serves as the basis for subsequent research. We introduce YOLO-Ant, a lightweight CNN and transformer hybrid detector specifically designed for antenna interference source detection. Specifically, we initially formulated a lightweight design for the network depth and width, ensuring that subsequent investigations were conducted within a lightweight framework. Then, we propose a DSLK-Block module based on depthwise separable convolution and large convolution kernels to enhance the network's feature extraction ability, effectively improving small object detection. To address challenges such as complex backgrounds and large interclass differences in antenna detection, we construct DSLKVit-Block, a powerful feature extraction module that combines DSLK-Block and transformer structures. Considering both its lightweight design and accuracy, our method not only achieves optimal performance on the antenna dataset but also yields competitive results on public datasets.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (70)
  1. X. Lu, J. Ji, Z. Xing, and Q. Miao, “Attention and feature fusion ssd for remote sensing object detection,” IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1–9, 2021.
  2. Z. Yang, Z. Xu, and Y. Wang, “Bidirection-fusion-yolov3: An improved method for insulator defect detection using uav image,” IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1–8, 2022.
  3. K. Hao, G. Chen, L. Zhao, Z. Li, Y. Liu, and C. Wang, “An insulator defect detection model in aerial images based on multiscale feature pyramid network,” IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1–12, 2022.
  4. K. Han, Y. Wang, Q. Tian, J. Guo, C. Xu, and C. Xu, “Ghostnet: More features from cheap operations,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020, pp. 1580–1589.
  5. M. Tan and Q. Le, “Efficientnetv2: Smaller models and faster training,” in International conference on machine learning.   PMLR, 2021, pp. 10 096–10 106.
  6. J.-S. Lim, M. Astrid, H.-J. Yoon, and S.-I. Lee, “Small object detection using context and attention,” in 2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC).   IEEE, 2021, pp. 181–186.
  7. X. Yang, J. Yang, J. Yan, Y. Zhang, T. Zhang, Z. Guo, X. Sun, and K. Fu, “Scrdet: Towards more robust detection for small, cluttered and rotated objects,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019, pp. 8232–8241.
  8. C. Yang, Z. Huang, and N. Wang, “Querydet: Cascaded sparse query for accelerating high-resolution small object detection,” in Proceedings of the IEEE/CVF Conference on computer vision and pattern recognition, 2022, pp. 13 668–13 677.
  9. S.-J. Ji, Q.-H. Ling, and F. Han, “An improved algorithm for small object detection based on yolo v4 and multi-scale contextual information,” Computers and Electrical Engineering, vol. 105, p. 108490, 2023. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0045790622007054
  10. Y. Cao, J. Xu, S. Lin, F. Wei, and H. Hu, “Gcnet: Non-local networks meet squeeze-excitation networks and beyond,” arXiv, 2019.
  11. N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, and S. Zagoruyko, “End-to-end object detection with transformers,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part I 16.   Springer, 2020, pp. 213–229.
  12. S. Zheng, J. Lu, H. Zhao, X. Zhu, Z. Luo, Y. Wang, Y. Fu, J. Feng, T. Xiang, P. H. Torr et al., “Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2021, pp. 6881–6890.
  13. G. Jocher, A. Stoken, A. Chaurasia, J. Borovec, NanoCode012, TaoXie, Y. Kwon, K. Michael, L. Changyu, J. Fang, A. V, Laughing, tkianai, yxNONG, P. Skalski, A. Hogan, J. Nadar, imyhxy, L. Mammana, AlexWang1900, C. Fati, D. Montes, J. Hajek, L. Diaconu, M. T. Minh, Marc, albinxavi, fatih, oleg, and wanghaoyang0106, “ultralytics/yolov5: v6.0 - YOLOv5n ’Nano’ models, Roboflow integration, TensorFlow export, OpenCV DNN support,” Oct. 2021. [Online]. Available: https://doi.org/10.5281/zenodo.5563715
  14. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE, vol. 86, no. 11, pp. 2278–2324, 1998.
  15. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Communications of the ACM, vol. 60, no. 6, pp. 84–90, 2017.
  16. K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556, 2014.
  17. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 1–9.
  18. K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770–778.
  19. R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2014, pp. 580–587.
  20. R. Girshick, “Fast r-cnn,” in Proceedings of the IEEE international conference on computer vision, 2015, pp. 1440–1448.
  21. R. Faster, “Towards real-time object detection with region proposal networks,” Advances in neural information processing systems, vol. 9199, no. 10.5555, pp. 2 969 239–2 969 250, 2015.
  22. J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 779–788.
  23. W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg, “Ssd: Single shot multibox detector,” in Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14.   Springer, 2016, pp. 21–37.
  24. J. Redmon and A. Farhadi, “Yolo9000: better, faster, stronger,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 7263–7271.
  25. ——, “Yolov3: An incremental improvement,” 2018.
  26. T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, “Focal loss for dense object detection,” in Proceedings of the IEEE international conference on computer vision, 2017, pp. 2980–2988.
  27. O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein et al., “Imagenet large scale visual recognition challenge,” International journal of computer vision, vol. 115, pp. 211–252, 2015.
  28. G. Ghaisi, T.-Y. Lin, R. Pang, and Q. V. L. NAS-FPN, “Learning scalable feature pyramid architecture for object detection.”
  29. M. Tan, R. Pang, and Q. V. Le, “Efficientdet: Scalable and efficient object detection,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020, pp. 10 781–10 790.
  30. A. Bochkovskiy, C.-Y. Wang, and H.-Y. M. Liao, “Yolov4: Optimal speed and accuracy of object detection,” arXiv preprint arXiv:2004.10934, 2020.
  31. C.-Y. Wang, A. Bochkovskiy, and H.-Y. M. Liao, “Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors,” arXiv preprint arXiv:2207.02696, 2022.
  32. J. Li, Y. Zhang, P. Yun, G. Zhou, Q. Chen, and R. Fan, “RoadFormer: Duplex transformer for rgb-normal semantic road scene parsing,” arXiv preprint arXiv:2309.10356, 2023.
  33. J. Hu, L. Shen, and G. Sun, “Squeeze-and-excitation networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 7132–7141.
  34. S. Woo, J. Park, J.-Y. Lee, and I. S. Kweon, “Cbam: Convolutional block attention module,” in Proceedings of the European conference on computer vision (ECCV), 2018, pp. 3–19.
  35. J. Fu, J. Liu, H. Tian, Y. Li, Y. Bao, Z. Fang, and H. Lu, “Dual attention network for scene segmentation,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2019, pp. 3146–3154.
  36. A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems, vol. 30, 2017.
  37. A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly et al., “An image is worth 16x16 words: Transformers for image recognition at scale,” arXiv preprint arXiv:2010.11929, 2020.
  38. Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, and B. Guo, “Swin transformer: Hierarchical vision transformer using shifted windows,” in Proceedings of the IEEE/CVF international conference on computer vision, 2021, pp. 10 012–10 022.
  39. S. Mehta and M. Rastegari, “Mobilevit: light-weight, general-purpose, and mobile-friendly vision transformer,” arXiv preprint arXiv:2110.02178, 2021.
  40. J. Pan, A. Bulat, F. Tan, X. Zhu, L. Dudziak, H. Li, G. Tzimiropoulos, and B. Martinez, “Edgevits: Competing light-weight cnns on mobile devices with vision transformers,” in Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XI.   Springer, 2022, pp. 294–311.
  41. D. Meng, X. Chen, Z. Fan, G. Zeng, H. Li, Y. Yuan, L. Sun, and J. Wang, “Conditional detr for fast training convergence,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 3651–3660.
  42. F. Li, H. Zhang, S. Liu, J. Guo, L. M. Ni, and L. Zhang, “Dn-detr: Accelerate detr training by introducing query denoising,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 13 619–13 627.
  43. J. Guo, K. Han, H. Wu, Y. Tang, X. Chen, Y. Wang, and C. Xu, “Cmt: Convolutional neural networks meet vision transformers,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 12 175–12 185.
  44. Z. Peng, W. Huang, S. Gu, L. Xie, Y. Wang, J. Jiao, and Q. Y. Conformer, “Local features coupling global representations for visual recognition. in 2021 ieee,” in CVF International Conference on Computer Vision, ICCV, 2021, pp. 357–366.
  45. Y. Chen, X. Dai, D. Chen, M. Liu, X. Dong, L. Yuan, and Z. Liu, “Mobile-former: Bridging mobilenet and transformer,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 5270–5279.
  46. A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam, “Mobilenets: Efficient convolutional neural networks for mobile vision applications,” arXiv preprint arXiv:1704.04861, 2017.
  47. Z. Liu, H. Mao, C.-Y. Wu, C. Feichtenhofer, T. Darrell, and S. Xie, “A convnet for the 2020s,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 11 976–11 986.
  48. X. Ding, X. Zhang, J. Han, and G. Ding, “Scaling up your kernels to 31x31: Revisiting large kernel design in cnns,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 11 963–11 975.
  49. X. Pan, C. Ge, R. Lu, S. Song, G. Chen, Z. Huang, and G. Huang, “On the integration of self-attention and convolution,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2022, pp. 815–825.
  50. H. Zhang, W. Hu, and X. Wang, “Parc-net: Position aware circular convolution with merits from convnets and transformer,” in Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXVI.   Springer, 2022, pp. 613–630.
  51. L. Guan, L. Jia, Z. Xie, and C. Yin, “A lightweight framework for obstacle detection in the railway image based on fast region proposal and improved yolo-tiny network,” IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1–16, 2022.
  52. Y. Dai, W. Liu, H. Wang, W. Xie, and K. Long, “Yolo-former: Marrying yolo and transformer for foreign object detection,” IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1–14, 2022.
  53. L. Wang, H. Qin, X. Zhou, X. Lu, and F. Zhang, “R-yolo: A robust object detector in adverse weather,” IEEE Transactions on Instrumentation and Measurement, vol. 72, pp. 1–11, 2022.
  54. M. A. A. Al-qaness, A. A. Abbasi, H. Fan, R. A. Ibrahim, S. H. Alsamhi, and A. Hawbani, “An improved yolo-based road traffic monitoring system,” vol. 103, pp. 211–230, 2021.
  55. F. Dang, D. Chen, Y. Lu, and Z. Li, “Yoloweeds: A novel benchmark of YOLO object detectors for multi-class weed detection in cotton production systems,” Comput. Electron. Agric., vol. 205, p. 107655, 2023. [Online]. Available: https://doi.org/10.1016/j.compag.2023.107655
  56. S. Guo et al., “UDTIRI: An online open-source intelligent road inspection benchmark suite,” IEEE Transactions on Intelligent Transportation Systems, DOI: 10.1109/TITS.2024.3351209.
  57. Z. Tian, C. Shen, H. Chen, and T. He, “Fcos: Fully convolutional one-stage object detection,” arXiv preprint arXiv:1904.01355, 2019.
  58. Z. Ge, S. Liu, F. Wang, Z. Li, and J. Sun, “Yolox: Exceeding yolo series in 2021,” arXiv preprint arXiv:2107.08430, 2021.
  59. C. Feng, Y. Zhong, Y. Gao, M. R. Scott, and W. Huang, “Tood: Task-aligned one-stage object detection,” 2021.
  60. W. Wang, E. Xie, X. Li, D.-P. Fan, K. Song, D. Liang, T. Lu, P. Luo, and L. Shao, “Pyramid vision transformer: A versatile backbone for dense prediction without convolutions,” in Proceedings of the IEEE/CVF international conference on computer vision, 2021, pp. 568–578.
  61. S. Liu, F. Li, H. Zhang, X. Yang, X. Qi, H. Su, J. Zhu, and L. Zhang, “Dab-detr: Dynamic anchor boxes are better queries for detr,” 2022.
  62. G. Jocher, A. Chaurasia, and J. Qiu, “YOLO by Ultralytics,” Jan. 2023. [Online]. Available: https://github.com/ultralytics/ultralytics
  63. H. Zhang, F. Li, S. Liu, L. Zhang, H. Su, J. Zhu, L. M. Ni, and H.-Y. Shum, “Dino: Detr with improved denoising anchor boxes for end-to-end object detection,” 2022.
  64. S. Woo, S. Debnath, R. Hu, X. Chen, Z. Liu, I. S. Kweon, and S. Xie, “Convnext v2: Co-designing and scaling convnets with masked autoencoders,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 16 133–16 142.
  65. W. Lv, Y. Zhao, S. Xu, J. Wei, G. Wang, C. Cui, Y. Du, Q. Dang, and Y. Liu, “Detrs beat yolos on real-time object detection,” 2023.
  66. C. Li, L. Li, H. Jiang, K. Weng, Y. Geng, L. Li, Z. Ke, Q. Li, M. Cheng, W. Nie, Y. Li, B. Zhang, Y. Liang, L. Zhou, X. Xu, X. Chu, X. Wei, and X. Wei, “Yolov6: A single-stage object detection framework for industrial applications,” 2022.
  67. X. Xu, Y. Jiang, W. Chen, Y. Huang, Y. Zhang, and X. Sun, “Damo-yolo: A report on real-time object detection design,” arXiv preprint arXiv:2211.15444, 2022.
  68. S. Xu, X. Wang, W. Lv, Q. Chang, C. Cui, K. Deng, G. Wang, Q. Dang, S. Wei, Y. Du, and B. Lai, “Pp-yoloe: An evolved version of yolo,” 2022.
  69. H. Yan, Z. Li, W. Li, C. Wang, M. Wu, and C. Zhang, “Contnet: Why not use convolution and transformer at the same time?” arXiv preprint arXiv:2104.13497, 2021.
  70. H. Zhang, Y. Wang, F. Dayoub, and N. Sünderhauf, “Varifocalnet: An iou-aware dense object detector,” arXiv preprint arXiv:2008.13367, 2020.
Citations (1)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now