An Improved YOLO Model for Traffic Signs Small Target Image Detection
Source abstract
Abstract
Traffic sign detection significantly reduces traffic accidents, but in real-world scenarios, the considerable distance between traffic signs and in-vehicle cameras means only a small proportion of traffic signs are captured in the images. This makes far-off traffic sign detection a small object detection problem, and with fewer details in small sign objects, detection becomes increasingly challenging. In this paper, we specifically address poor localization, low accuracy, and missed detections when using You Only Look Once Version 5 (YOLOv5) for detecting small traffic sign objects. Firstly, we incorporate a decoupled head into YOLOv5’s detection algorithm, which serves to improve detection accuracy and accelerate network convergence. Then, to handle low-resolution targets better, we substitute the network’s original convolution layers with Space-to-Depth Convolution (SPD-Conv) modules. This modification enhances the model’s capacity to extract features from low-resolution traffic sign objects. Lastly, we integrate the Context Augmentation Module (CAM) into YOLOv5 by employing variable rate extended convolution. This module extracts context information from multiple receptive fields, thus providing essential supplementary information and significantly enhancing detection accuracy. Empirical results demonstrate the efficacy of our algorithm, shown by a substantial increase in object detection precision rate to 95.0%, a recall rate of 91.6%, and an average precision of 95.4%. These results represent improvements of 2.1%, 4.8% and 3.7%, respectively, when compared to the original YOLOv5 algorithm. Furthermore, when tested against other state-of-the-art methods, our proposed methodology shows superior performance.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Neural Network Applications · Advanced Image and Video Retrieval Techniques · Multimodal Machine Learning Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Process Equipment and Control Engineering, School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China
School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China
References (33)
Show source-provided references
1. Deng, X. (2014). Research on Detection and Recognition of Road Traffic Signs in Natural Environments. [Ph.D. Thesis, Nanjing University of Science and Technology].
2. Vision-Based Traffic Sign Detection and Analysis for Intelligent Driver Assistance Systems: Perspectives and Survey10.1109/TITS.2012.2209421 ↗
3. Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23–28). Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, USA.10.1109/CVPR.2014.81 ↗
4. Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27–30). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.10.1109/CVPR.2016.91 ↗
5. Tan, M., Pang, R., and Le, Q.V. (2020, January 13–19). EfficientDet: Scalable and Efficient Object Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.10.1109/CVPR42600.2020.01079 ↗
6. Leibe, B., Matas, J., Sebe, N., and Welling, M. (2016, January 11–14). SSD: Single Shot MultiBox Detector. Proceedings of the Computer Vision—ECCV 2016, Amsterdam, The Netherlands.10.1007/978-3-319-46478-7 ↗
7. Girshick, R. (2015, January 7–13). Fast R-CNN. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.10.1109/ICCV.2015.169 ↗
8. Cortes, C., Lawrence, N., Lee, D., Sugiyama, M., and Garnett, R. (2015, January 7–12). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada.
9. Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition10.1109/TPAMI.2015.2389824 ↗
10. Kong, T., Yao, A., Chen, Y., and Sun, F. (2016, January 27–30). HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.10.1109/CVPR.2016.98 ↗
11. Schoeffmann, K., Chalidabhongse, T.H., Ngo, C.W., Aramvith, S., O’Connor, N.E., Ho, Y.S., Gabbouj, M., and Elgammal, A. (2018, January 5–7). LOCO: Local Context Based Faster R-CNN for Small Traffic Sign Detection. Proceedings of the MultiMedia Modeling, Bangkok, Thailand.10.1007/978-3-319-73603-7 ↗
12. Recent advances in small object detection based on deep learning: A review10.1016/j.imavis.2020.103910 ↗
13. Traffic sign detection algorithm based on improved YOLOv4-Tiny10.1016/j.image.2022.116783 ↗
14. Zhu, Z., Liang, D., Zhang, S., Huang, X., Li, B., and Hu, S. (2016, January 27–30). Traffic-Sign Detection and Classification in the Wild. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.10.1109/CVPR.2016.232 ↗
15. Houben, S., Stallkamp, J., Salmen, J., Schlipsing, M., and Igel, C. (2013, January 4–9). Detection of traffic signs in real-world images: The German Traffic Sign Detection Benchmark. Proceedings of the 2013 International Joint Conference on Neural Networks (IJCNN), Dallas, TX, USA.10.1109/IJCNN.2013.6706807 ↗
16. Lai, H., Chen, L., Liu, W., Yan, Z., and Ye, S. (2023). STC-YOLO: Small Object Detection Network for Traffic Signs in Complex Environments. Sensors, 23.10.3390/s23115307 ↗
17. Liu, J., Zhang, W., Tang, Y., Tang, J., and Wu, G. (2020, January 13–19). Residual Feature Aggregation Network for Image Super-Resolution. Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.10.1109/CVPR42600.2020.00243 ↗
18. Kim, C.-I., Park, J., Park, Y., Jung, W., and Lim, Y.-S. (2023). Deep Learning-Based Real-Time Traffic Sign Recognition System for Urban. Environ. Infrastruct., 8.10.3390/infrastructures8020020 ↗
19. Du, Y., Zhao, Z., Song, Y., Zhao, Y., Su, F., Gong, T., and Meng, H. (2023). StrongSORT: Make DeepSORT Great Again. arXiv.10.1109/TMM.2023.3240881 ↗
20. Chu, J., Zhang, C., Yan, M., Zhang, H., and Ge, T. (2023). TRD-YOLO: A Real-Time, High-Performance Small Traffic Sign Detection Algorithm. Sensors, 23.10.3390/s23083871 ↗
21. Hu, J., Wang, Z., Chang, M., Xie, L., Xu, W., and Chen, N. (2022). PSG-YOLOv5: A Paradigm for Traffic Sign Detection and Recognition Algorithm Based on Deep Learning. Symmetry, 14.10.3390/sym14112262 ↗
22. Li, H., Li, J., Wei, H., Liu, Z., Zhan, Z., and Ren, Q. (2022). Slim-neck by GSConv: A better design paradigm of detector architectures for autonomous vehicles. arXiv.
23. Traffic Sign Recognition Using a Multi-Task Convolutional Neural Network10.1109/TITS.2017.2714691 ↗
24. A Traffic-Sign Detection Algorithm Based on Improved Sparse R-cnn10.1109/ACCESS.2021.3109606 ↗
25. Xiong, Q., Zhang, X., Wang, X., Qiao, N., and Shen, J. (2022). Robust Iris-Localization Algorithm in Non-Cooperative Environments Based on the Improved YOLO v4 Model. Sensors, 22.10.3390/s22249913 ↗
26. Ge, Z., Liu, S., Wang, F., Li, Z., and Sun, J. (2021). YOLOX: Exceeding YOLO Series in 2021. arXiv.
27. Bochkovskiy, A., Wang, C.Y., and Liao, H.Y.M. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection. arXiv.
28. He, K., Zhang, X., Ren, S., and Sun, J. (2014, January 6–12). Spatial pyramid pooling in deep convolutional networks for visual recognition. Proceedings of the European Conference on Computer Vision—ECCV 2014, Zurich, Switzerland.10.1007/978-3-319-10578-9_23 ↗
29. Wang, C.-Y., Liao, H.-Y.M., Wu, Y.-H., Chen, P.-Y., Hsieh, J.-W., and Yeh, I.-H. (2019). CSPNet: A New Backbone that can Enhance Learning Capability of CNN. arXiv.10.1109/CVPRW50498.2020.00203 ↗
30. Liu, S., Qi, L., Qin, H., Shi, J., and Jia, J. (2018). Path Aggregation Network for Instance Segmentation. arXiv.10.1109/CVPR.2018.00913 ↗
31. Sunkara, R., and Luo, T. (2022). No More Strided Convolutions or Pooling: A New CNN Building Block for Low-Resolution Images and Small Objects. arXiv.10.1007/978-3-031-26409-2_27 ↗
32. Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017). Feature pyramid networks for object detection. arXiv.10.1109/CVPR.2017.106 ↗
33. Xiao, J., Zhao, T., Yao, Y., Yu, Q., and Chen, Y. (2022, January 25–29). Context Augmentation and Feature Refinement Network for Tiny Object Detection. Proceedings of the 10th International Conference on Learning Representations, Online.