改进YOLOv5-S的交通标志检测算法

doi:10.3778/j.issn.1002-8331.2306-0293

摘要/Abstract

摘要： 在自动驾驶领域，现有的交通标志检测方法在检测复杂背景中的标志时存在着漏检或误检的问题，降低了智能汽车的可靠性。对此，提出了一种改进YOLOv5-S的实时交通标志检测算法。在特征提取网络中融合坐标注意力机制，通过构建目标的长范围依赖来捕获物体的位置感知，使得算法聚焦于重点的特征区域；引入Focal-EIoU损失函数来取代CIoU，使其更关注高质量的分类样本，提高对难分类样本的学习能力，减少漏检或者误检的问题；在网络中融合轻量级卷积技术GSConv，降低模型的计算量。增加新的小目标检测层，通过更丰富的特征信息提高小尺寸标志的检测效果。实验结果表明，改进方法的mAP@0.5和mAP@0.5：0.95分别为88.1%和68.5%，检测速度达到了83?FPS，能够满足实时可靠的检测需求。

关键词: 交通标志检测, YOLOv5, 坐标注意机制, Focal-EIoU, GSConv

Abstract: In the field of autonomous driving, existing traffic sign detection methods have problems with missed or incorrect sign detection in complex backgrounds, reducing the reliability of intelligent vehicles. To address this issue, a real-time traffic sign detection algorithm is proposed to enhance YOLOv5-S. Firstly, the coordinate attention mechanism is integrated into the feature extraction network to perceive the location of the object by establishing long-term dependencies on the target, making the algorithm focus on high-priority regions. Secondly, the Focal-EIoU loss function is used to replace the CIoU, allowing the network to focus more on high-quality classification samples, improving the network’s ability to learn from difficult samples and reducing the occurrence of missed or false detections. Next, the lightweight convolution technique GSConv is integrated into the network to reduce the complexity of the model. Finally, a new small target detection layer is added to improve the algorithm’s detection of small-sized signs by using richer feature information. The experimental results show that the improves algorithm achieves 88.1% for mAP@0.5 and 68.5% for mAP@0.5：0.95, with a detection speed of 83 FPS, which can meet the requirements of real-time and reliable detection.

Key words: traffic sign detection, YOLOv5, coordinate attention mechanism, Focal-EIoU, GSConv

刘海斌, 张友兵, 周奎, 张宇丰, 吕圣. 改进YOLOv5-S的交通标志检测算法[J]. 计算机工程与应用, 2024, 60(5): 200-209.

LIU Haibin, ZHANG Youbing, ZHOU Kui, ZHANG Yufeng, LYU Sheng. Traffic Sign Detection Algorithm Based on Improved YOLOv5-S[J]. Computer Engineering and Applications, 2024, 60(5): 200-209.

参考文献

[1] 马永杰, 程时升, 马芸婷, 等. 卷积神经网络及其在智能交通系统中的应用综述[J]. 交通运输工程学报, 2021, 21(4): 48-71.
MA Y J, CHENG S S, MA Y T, et al. Review of convolutional neural network and its application in intelligent transportation system[J]. Journal of Traffic and Transportation Engineering, 2021, 21(4): 48-71.
[2] RADU M D, COSTEA I M, STAN V A. Automatic traffic sign recognition artificial inteligence-deep learning algorithm[C]//Proceedings of the 12th International Conference on Electronics, Computers and Artificial Intelligence, 2020: 1-4.
[3] MOGELMOSE A, TRIVEDI M M, MOESLUND T B. Vision-based traffic sign detection and analysis for intelligent driver assistance systems: perspectives and survey[J]. IEEE Transactions on Intelligent Transportation Systems, 2012, 13(4): 1484-1497.
[4] SAADNA Y, BEHLOUL A. An overview of traffic sign detection and classification methods[J]. International Journal of Multimedia Information Retrieval, 2017, 6: 193-210.
[5] GIRSHICK R. Fast R-CNN[C]//Proceedings of the IEEE International Conference on Computer Vision, 2015: 1440-1448.
[6] REN S, HE K, GIRSHICK R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137-1149.
[7] HE K, GKIOXARI G, DOLLáR P, et al. Mask R-CNN[C]//Proceedings of the IEEE International Conference on Computer Vision, 2017: 2961-2969.
[8] 张毅, 龚致远, 韦文闻. 基于改进Faster R-CNN模型的交通标志检测[J]. 激光与光电子学进展, 2020, 57(18): 173-181.
ZHANG Y, GONG Z Y, WEI W W. Traffic sign detection based on improved faster R-CNN model[J]. Laser and Optoelectronics Progress, 2020, 57(18): 173-181.
[9] HAN C, GAO G, ZHANG Y. Real-time small traffic sign detection with revised faster-RCNN[J]. Multimedia Tools and Applications, 2019, 78: 13263-13278.
[10] LIU W, ANGUELOV D, ERHAN D, et al. SSD: single shot multibox detector[C]//Proceedings of the 14th European Conference on Computer Vision, 2016: 21-37.
[11] REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real-time object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016: 779-788.
[12] REDMON J, FARHADI A. YOLO9000: better, faster, stronger[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 7263-7271.
[13] REDMON J, FARHADI A. YOLOv3: an incremental improvement[J]. arXiv:1804.02767,2018.
[14] BOCHKOVSKIY A, WANG C Y, LIAO H Y M. YOLOv4: optimal speed and accuracy of object detection[J]. arXiv:2004.10934,2020.
[15] GE Z, LIU S, WANG F, et al. YOLOx: exceeding yolo series in 2021[J]. arXiv:1606.08415,2016.
[16] LI C, LI L, JIANG H, et al. YOLOv6: a single-stage object detection framework for industrial applications[J]. arXiv:2209.02976,2022.
[17] 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[J]. arXiv:2207.02696,2022.
[18] XU X, JIANG Y, CHEN W, et al. Damo-YOLO: a report on real-time object detection design[J]. arXiv:2211.15444,2022.
[19] 刘紫燕, 袁磊, 朱明成, 等. 融合SPP和改进FPN的YOLOv3交通标志检测[J]. 计算机工程与应用, 2021, 57(7): 164-170.
LIU Z Y, YUAN L, ZHU M C, et al. YOLOv3 traffic sign detection based on SPP and improved FPN[J]. Computer Engineering and Applications, 2021, 57(7): 164-170.
[20] HE K, ZHANG X, REN S, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9): 1904-1916.
[21] LIN T Y, DOLLáR P, GIRSHICK R, et al. Feature pyramid networks for object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 2117-2125.
[22] FAN W, YI N, HU Y. A Traffic sign recognition method based on improved YOLOv3[C]//Advances in Intelligent Automation and Soft Computing, 2022: 846-853.
[23] 冯爱棋, 吴小俊, 徐天阳. 融合注意力机制和上下文信息的实时交通标志检测算法[J]. 计算机科学与探索, 2023, 17(11): 2676-2688.
FENG A Q, WU X J, XU T Y. Real-time traffic sign detection algorithm combining attention mechanism and contextual information[J]. Journal of Frontiers of Computer Science and Technology, 2023, 17(11): 2676-2688.
[24] 俞林森, 陈志国. 融合前景注意力的轻量级交通标志检测网络[J]. 电子测量与仪器学报, 2023, 37(1): 21-31.
YU L S, CHEN Z G. Lightweight traffic sign detection network with fused foreground attention[J]. Journal of Electronic Measurement and Instrumentation, 2023, 37(1): 21-31.
[25] HOU Q, ZHOU D, FENG J. Coordinate attention for efficient mobile network design[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021: 13713-13722.
[26] ZHANG Y F, REN W, ZHANG Z, et al. Focal and efficient IOU loss for accurate bounding box regression[J]. Neurocomputing, 2022, 506: 146-157.
[27] LI H, LI J, WEI H, et al. Slim-neck by GSConv: a better design paradigm of detector architectures for autonomous vehicles[J]. arXiv:2206.02424,2022.
[28] REZATOFIGHI H, TSOI N, GWAK J, et al. Generalized intersection over union: a metric and a loss for bounding box regression[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognitio, 2019: 658-666.
[29] ZHENG Z, WANG P, LIU W, et al. Distance-IoU loss: faster and better learning for bounding box regression[C]//Proceedings of the AAAI Conference on Artificial Intelligence, 2020: 12993-13000.
[30] HU J, SHEN L, SUN G. Squeeze-and-excitation networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018: 7132-7141.
[31] WOO S, PARK J, LEE J Y, et al. CBAM: convolutional block attention module[C]//Proceedings of the European Conference on Computer Vision (ECCV), 2018: 3-19.
[32] LIN T Y, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection[C]//Proceedings of the IEEE International Conference on Computer Vision, 2017: 2980-2988.
[33] LIU S, QI L, QIN H, et al. Path aggregation network for instance segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018: 8759-8768.
[34] ZHU Z, LIANG D, ZHANG S, et al. Traffic-sign detection and classification in the wild[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016: 2110-2118.
[35] WANG Q, WU B, ZHU P, et al. ECA-Net: efficient channel attention for deep convolutional neural networks[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020: 11534-11542.
[36] LI X, WANG W, HU X, et al. Selective kernel networks[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019: 510-519.
[37] LIU Y, SHAO Z, TENG Y, et al. NAM: normalization-based attention module[J]. arXiv:2111.12419,2021.
[38] 胡均平, 王鸿树, 戴小标, 等. 改进YOLOv5的小目标交通标志实时检测算法[J]. 计算机工程与应用, 2023, 59(2): 185-193.
HU J P, WANG H S, DAI X B, et al. Real-time detection algorithm for small-target traffic signs based on improved YOLOv5[J]. Computer Engineering and Applications, 2023, 59(2): 185-193.
[39] 尹宋麟, 谭飞, 周晴, 等. 基于改进YOLOv4模型的交通标志检测[J]. 无线电工程, 2022, 52(11): 2087-2093.
YIN S L, TAN F, ZHOU Q, et al. Traffic sign detection based on improved YOLOv4 model[J]. Radio Engineering, 2022, 52(11): 2087-2093.
[40] 韦强, 胡晓阳, 赵虹鑫. 改进YOLOv5的交通标志检测方法[J]. 计算机工程与应用, 2023, 59(13): 229-237.
WEI Q, HU X Y, ZHAO H X. Improved traffic sign detection method for YOLOv5[J]. Computer Engineering and Applications, 2023, 59(13): 229-237.
[41] 郎斌柯, 吕斌, 吴建清, 等. 基于CA-BIFPN的交通标志检测模型[J]. 深圳大学学报(理工版), 2023, 40(3): 335-343.
LANG B K, LVY B, WU J Q, et al. A traffic sign detection model based on attention mechanism and spatial pyramid[J]. Journal of Shenzhen University Science and Engineering, 2023, 40(3): 335-343.