YOLOv11 for Vehicle Detection: Advancements, Performance, and Applications in Intelligent Transportation Systems (2410.22898v1)
Abstract: Accurate vehicle detection is essential for the development of intelligent transportation systems, autonomous driving, and traffic monitoring. This paper presents a detailed analysis of YOLO11, the latest advancement in the YOLO series of deep learning models, focusing exclusively on vehicle detection tasks. Building upon the success of its predecessors, YOLO11 introduces architectural improvements designed to enhance detection speed, accuracy, and robustness in complex environments. Using a comprehensive dataset comprising multiple vehicle types-cars, trucks, buses, motorcycles, and bicycles we evaluate YOLO11's performance using metrics such as precision, recall, F1 score, and mean average precision (mAP). Our findings demonstrate that YOLO11 surpasses previous versions (YOLOv8 and YOLOv10) in detecting smaller and more occluded vehicles while maintaining a competitive inference time, making it well-suited for real-time applications. Comparative analysis shows significant improvements in the detection of complex vehicle geometries, further contributing to the development of efficient and scalable vehicle detection systems. This research highlights YOLO11's potential to enhance autonomous vehicle performance and traffic monitoring systems, offering insights for future developments in the field.
- On-road vehicle detection: A review. IEEE transactions on pattern analysis and machine intelligence, 28(5):694–711, 2006.
- V Vijayaraghavan and M Laavanya. Vehicle classification and detection using deep learning. Int. J. Eng. Adv. Technol, 9:24–28, 2019.
- K O’Shea. An introduction to convolutional neural networks. arXiv preprint arXiv:1511.08458, 2015.
- Mujadded Al Rabbani Alif and Muhammad Hussain. Lightweight convolutional network with integrated attention mechanism for missing bolt detection in railways. Metrology, 4(2):254–278, 2024.
- Mujadded Al Rabbani Alif. State-of-the-art bangla handwritten character recognition using a modified resnet-34 architecture. Int. J. Innov. Sci. Res. Technol, 9:438–448, 2024.
- Isolated bangla handwritten character recognition with convolutional neural network. In 2017 20th International conference of computer and information technology (ICCIT), pages 1–6. IEEE, 2017.
- Mujadded Al Rabbani Alif. Enhancing diabetic retinopathy diagnosis: A lightweight cnn architecture for efficient exudate detection in retinal fundus images. arXiv preprint arXiv:2408.06784, 2024.
- Mujadded Al Rabbani Alif. Enhancing construction site safety: A lightweight convolutional network for effective helmet detection. arXiv preprint arXiv:2409.12669, 2024.
- N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), volume 1, pages 886–893 vol. 1, 2005.
- Support-vector networks. Machine learning, 20(3):273–297, 1995.
- Speeded-up robust features (surf). Computer vision and image understanding, 110(3):346–359, 2008.
- Exudate regeneration for automated exudate detection in retinal fundus images. IEEE access, 11:83934–83945, 2022.
- Rapid object detection using a boosted cascade of simple features. In Proceedings of the 2001 IEEE computer society conference on computer vision and pattern recognition. CVPR 2001, volume 1, pages I–I. Ieee, 2001.
- Domain modelling for a lightweight convolutional network focused on automated exudate detection in retinal fundus images. In 2023 9th International Conference on Information Technology Trends (ITT), pages 145–150. IEEE, 2023.
- You only look once: Unified, real-time object detection. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 779–788, 2016.
- Region-based convolutional networks for accurate object detection and segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(1):142–158, 2016.
- Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems, 28, 2015.
- Mujadded Al Rabbani Alif and Muhammad Hussain. Yolov1 to yolov10: A comprehensive review of yolo variants and their application in the agricultural domain. arXiv preprint arXiv:2406.10139, 2024.
- Yolo9000: Better, faster, stronger. arxiv. arXiv preprint arXiv:1612.08242, 394, 2016.
- Joseph Redmon. Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767, 2018.
- Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934, 2020.
- ultralytics/yolov5: v7.0 - YOLOv5 SOTA Realtime Instance Segmentation, November 2022.
- Yolov6: A single-stage object detection framework for industrial applications. arXiv preprint arXiv:2209.02976, 2022.
- Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors, 2022.
- Ultralytics yolov8, 2023.
- An image is worth 16x16 words: Transformers for image recognition at scale, 2021.
- Boltvision: A comparative analysis of cnn, cct, and vit in achieving high accuracy for missing bolt classification in train components. Machines, 12(2):93, 2024.
- Mujadded Al Rabbani Alif. Attention-based automated pallet racking damage detection. Int. J. Innov. Sci. Res. Technol, 9:728–740, 2024.
- Ultralytics yolo11, 2024.
- Child emotion recognition via custom lightweight cnn architecture. In Kids Cybersecurity Using Computational Intelligence Techniques, pages 165–174. Springer, 2023.
- Comparative analysis of yolov8 and yolov10 in vehicle detection: Performance metrics and model efficacy. Vehicles, 6(3):1364–1382, 2024.
- Ankan Ghosh. Yolo11: Faster than you can imagine!, Oct 2024.
- A gradient guided architecture coupled with filter fused representations for micro-crack detection in photovoltaic cell surfaces. IEEE Access, 10:58950–58964, 2022.
- A dynamic multi-mobile agent itinerary planning approach in wireless sensor networks via intuitionistic fuzzy set. Sensors, 22(20):8037, 2022.