ABSTRACT
Since traffic light detection is essential for autonomous driving, it is studied intensively. However, traffic sign detection is difficult, especially in a complex environment. The traffic signs should be located first. Their unique features should be extracted next and fed into the classifier subsequently. In this paper, we adopt the current mainstream deep neural network-based object detection method for traffic sign detection. In our work, add specific environmental noise features to the dataset. A lightweight network, YOLOv4-Tiny, is chosen as the baseline network, and a multi-scale feature fusion module is designed to improve the performance of the network model. A lightweight group attention module is also designed. Experiments are carried out using the GTSDB dataset and the result shows the proposed model outperforms the other models in terms of precision and mAP.
- Huang H, Hou L Y. Speed Limit Sign Detection Based on Gaussian Color Model and Template Matching[C]// In 2017 International Conference on Vision, Image and Signal Processing (ICVISP). IEEE, 2017: 118-122.Google Scholar
- Zhe X, Jingyi R, Chaoqian B. A Traffic signs' detection method of contour approximation based on concave removal[C]// In 2016 Chinese Control and Decision Conference (CCDC). IEEE, 2016: 5199-5204.Google Scholar
- Wang C. Research and Application of Traffic Sign Detection and Recognition Based on Deep Learning[C]// 2018 International Conference on Robots & Intelligent System (ICRIS). IEEE, 2018: 150-152.Google Scholar
- Jiao L, Zhang F, Liu F, A Survey of Deep Learning-Based Object Detection[J]. IEEE Access, 2019, 7: 128837-128868.Google ScholarCross Ref
- Paclik P, Novovieova J, Pudil P. Road sign classification using Laplace kernel classifier[J]. Pattern Recognition Letters, 2000, 21(13/14):1165-1173.Google ScholarDigital Library
- Lecun Y, Bottou L, Bengio Y, Gradient-Based Learning Applied to Document Recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324.Google ScholarCross Ref
- Krizhevsky A, Sutskever I, Hinton G E. ImageNet Classification with Deep Convolutional Neural Networks[J]. Communications of the ACM, 2017, 60(6): 84-90.Google ScholarDigital Library
- He K, Zhang X, Ren S, Deep Residual Learning for Image Recognition[C]// 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE Computer Society, 2016: 770-778.Google Scholar
- Redmon J, Divvala S, Girshick R, You Only Look Once: Unified, Real-Time Object Detection[C]// Computer Vision and Pattern Recognition (CVPR). IEEE Computer Society, 2016: 779-788.Google Scholar
- Bochkovskiy A, Wang C Y, Liao H. YOLOv4: Optimal Speed and Accuracy of Object Detection[J]. 2020.Google Scholar
- He K, Zhang X, Ren S, Sun J. Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE transactions on pattern analysis and machine intelligence. 2015 Jan 9;37(9):1904-16.Google ScholarDigital Library
- Ren S, He K, Girshick R, Sun J. Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems. 2015;28:91-9.Google Scholar
- He K, Gkioxari G, Dollár P, Girshick R. Mask r-cnn. InProceedings of the IEEE international conference on computer vision 2017 (pp. 2961-2969).Google Scholar
- Wei L, Anguelov D, Erhan D, SSD: single shot multi-box detector[C]// Proceedings of 14th European Conference on Computer Vision (ECCV). 2016: 21-37.Google Scholar
- Hou Q, Zhou D, Feng J. Coordinate attention for efficient mobile network design. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2021 (pp. 13713-13722).Google Scholar
- Hu J, Shen L, Sun G. Squeeze-and-excitation networks. InProceedings of the IEEE conference on computer vision and pattern recognition 2018 (pp. 7132-7141).Google Scholar
- Woo S, Park J, Lee JY, Kweon IS. Cbam: Convolutional block attention module. InProceedings of the European conference on computer vision (ECCV) 2018 (pp. 3-19).Google Scholar
- Liu H, Liu F, Fan X, Huang D. Polarized self-attention: Towards high-quality pixel-wise regression. arXiv preprint arXiv:2107.00782. 2021 Jul 2.Google Scholar
- Li Y, Chen Y, Wang N, Zhang Z. Scale-aware trident networks for object detection. InProceedings of the IEEE/CVF International Conference on Computer Vision 2019 (pp. 6054-6063).Google Scholar
- Ren K, Huang L, Fan C, Han H, Deng H. Real-time traffic sign detection network using DS-DetNet and lite fusion FPN. Journal of Real-Time Image Processing. 2021 Apr 18:1-1.Google Scholar
Recommendations
Traffic Sign Detection based on SSD
CACRE2019: Proceedings of the 2019 4th International Conference on Automation, Control and Robotics EngineeringThe traffic sign recognition process includes two parts: detection and classification. In this paper, we use an object detection algorithm called SSD to detect the traffic signs. This convolutional neural network uses multiple feature maps to detect ...
MTSDet: multi-scale traffic sign detection with attention and path aggregation
AbstractTo solve the problem that existing traffic signs are not easily detected leading to low detection performance due to their small sizes and external factors such as weather conditions, this paper proposes a traffic sign detection method, MTSDet (...
Traffic sign detection algorithm based on feature expression enhancement
AbstractTraffic sign detection is an important research direction in computer vision, which is of great significance for autonomous driving and advanced assisted driving systems. Due to the complexity of traffic signs in natural scenes, existing traffic ...
Comments