Traffic Light Recognition Method Based on Improved YOLOv5s

doi:10.19678/j.issn.1000-3428.0062843

Abstract

Abstract: The detection and recognition of traffic lights are critical to improving the safety of unmanned driving systems.Existing recognition methods based on deep learning cannot achieve a good balance between accuracy and speed;hence, it is difficult to satisfy the detection requirements in actual environments.YOLOv5 has the advantage of a small network scale and is suitable for traffic signal detection in traffic scenarios.This study proposes the TL-YOLOV5s network for traffic signal light recognition by improving the YOLOv5 network.By simplifying the number of convolutional layers in the backbone network, the efficiency of feature extraction is improved.In addition, the residual components are densely connected and multilevel cross-connected, and two new CSP residual structures are obtained to replace the residual structure in the original network;thus, the feature fusion ability of the network is strengthened, and the recognition accuracy is improved.Based on the small target attribute of traffic lights, the detection scales of small and medium targets are retained in the network, and the detection scale of large targets is removed to further improve the recognition rate.The experiment is conducted for a LaRA dataset of traffic lights in Paris, France.The results show that the mAP value of the TL-YOLOV5s network reaches 70.1%, which is 6.3 percentage points higher than that of baseline network, YOLOv5.Furthermore, the detection speed reaches 22.4 frame/s, which can satisfy the real-time requirements in actual environments.

Key words: traffic light, target detection, deep learning, image processing, small-scale target

摘要： 交通信号灯的检测与识别是提升无人驾驶系统安全性的关键技术，传统基于深度学习的识别方法不能在精度和速度之间达到较好的平衡，难以满足实际环境下的检测要求。YOLOv5具有网络规模小的优势，适合在交通场景下进行交通信号灯检测。对YOLOv5网络进行改进，提出TL-YOLOv5s网络用于交通信号灯识别。通过简化主干网络中卷积层的数量提高特征提取效率，同时对残差组件进行密集连接和多层次跨连接，得到2种新的CSP残差结构替换原网络中的残差结构，增强网络特征融合能力，提高识别精度。考虑到交通信号灯的小目标属性，在网络中保留中小目标检测尺度而去除大目标检测尺度，进一步提升识别速率。在法国巴黎LaRA信号灯数据集上进行实验，结果表明，TL-YOLOv5s网络mAP值达到70.1%，相比于基线网络YOLOv5提升6.3个百分点，且检测速度达到22.4 frame/s，能够满足现实环境下的实时性要求。

关键词: 交通信号灯, 目标检测, 深度学习, 图像处理, 小尺度目标

CLC Number:

TP18

DENG Tianmin, TAN Siqi, PU Longzhong. Traffic Light Recognition Method Based on Improved YOLOv5s[J]. Computer Engineering, 2022, 48(9): 55-62.

邓天民, 谭思奇, 蒲龙忠. 基于改进YOLOv5s的交通信号灯识别方法[J]. 计算机工程, 2022, 48(9): 55-62.

/ / Recommend / Download Citations

URL: http://www.ecice06.com/EN/10.19678/j.issn.1000-3428.0062843

http://www.ecice06.com/EN/Y2022/V48/I9/55

Figures/Tables 21

References

[1] 音松, 陈雪云, 贝学宇.改进Mask RCNN算法及其在行人实例分割中的应用[J].计算机工程, 2021, 47(6):271-276, 283. YIN S, CHEN X Y, BEI X Y.Improved Mask RCNN algorithm and its application in pedestrian instance segmentation[J].Computer Engineering, 2021, 47(6):271-276, 283.(in Chinese)
[2] 喻清挺, 喻维超, 喻国平.基于改进R-FCN的交通标志检测[J].计算机工程, 2021, 47(12):285-290, 298. YU Q T, YU W C, YU G P.Traffic sign detection based on improved R-FCN[J].Computer Engineering, 2021, 47(12):285-290, 298.(in Chinese)
[3] GIRSHICK R, DONAHUE J, DARRELL T, et al.Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2014:580-587.
[4] GIRSHICK R.Fast R-CNN[C]//Proceedings of 2015 IEEE International Conference on Computer Vision.Washington D.C., USA:IEEE Press.2015:1440-1448.
[5] REN S Q, HE K M, 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.
[6] 李昊.基于深度学习的复杂环境下交通信号灯检测算法研究[D].郑州:郑州大学, 2018. LI H.Research on traffic signal detection algorithm based on deep learning in complex environment[D].Zhengzhou:Zhengzhou University, 2018.(in Chinese)
[7] REDMON J, DIVVALA S, GIRSHICK R, et al.You only look once:unified, real-time object detection[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2016:779-788.
[8] REDMON J, FARHADI A.YOLOv3:an incremental improvement[EB/OL].[2021-07-28].https://arxiv.org/abs/1804.02767.
[9] BOCHKOVSKIY A, WANG C Y, LIAO H Y M.YOLOv4:optimal speed and accuracy of object detection[EB/OL].[2021-07-28].https://arxiv.org/abs/2004.10934.
[10] LIU W, ANGUELOV D, ERHAN D, et al.SSD:single shot multibox detector[C]//Proceedings of European Conference on Computer Vision.Berlin, Germany:Springer, 2016:21-37.
[11] FU C Y, LIU W, RANGA A, et al.DSSD:deconvolutional single shot detector[EB/OL].[2021-07-28].https://arxiv.org/abs/1701.06659.
[12] IANDOLA F N, HAN S, MOSKEWICZ M W, et al.SqueezeNet:AlexNet-level accuracy with 50x fewer parameters and <0.5 MB model size[EB/OL].[2021-07-28].https://arxiv.org/pdf/1602.07360.
[13] 钱弘毅, 王丽华, 牟宏磊.基于深度学习的交通信号灯快速检测与识别[J].计算机科学, 2019, 46(12):272-278. QIAN H Y, WANG L H, MOU H L.Fast detection and identification of traffic lights based on deep learning[J].Computer Science, 2019, 46(12):272-278.(in Chinese)
[14] 鞠默然, 罗海波, 王仲博, 等.改进的YOLO V3算法及其在小目标检测中的应用[J].光学学报, 2019, 39(7):253-260. JU M R, LUO H B, WANG Z B, et al.Improved YOLO V3 algorithm and its application in small target detection[J].Acta Optica Sinica, 2019, 39(7):253-260.(in Chinese)
[15] 毛涛.基于YOLO的交通信号灯检测算法[J].数字技术及应用, 2021, 39(6):97-99. MAO T.Traffic signal detection algorithm based on YOLO[J].Digital Technology & Application, 2021, 39(6):97-99.(in Chinese)
[16] HE K M, ZHANG X Y, REN S Q, 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.
[17] LIN T Y, DOLLÁR P, GIRSHICK R, et al.Feature pyramid networks for object detection[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2017:936-944.
[18] LIU S, QI L, QIN H F, et al.Path aggregation network for instance segmentation[C]//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2018:8759-8768.
[19] ZHENG Z H, WANG P, LIU W, et al.Distance-IoU loss:faster and better learning for bounding box regression[C]//Proceedings of AAAI Conference on Artificial Intelligence.Palo Alto, USA:AAAI Press, 2020:12993-13000.
[20] WANG C Y, MARK L H Y, WU Y H, et al.CSPNet:a new backbone that can enhance learning capability of CNN[C]//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2020:1571-1580.
[21] HE K M, ZHANG X Y, REN S Q, et al.Deep residual learning for image recognition[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2016:770-778.
[22] HUANG G, LIU Z, VAN DER MAATEN L, et al.Densely connected convolutional networks[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2017:2261-2269.
[23] 王艺皓, 丁洪伟, 李波, 等.复杂场景下基于改进YOLOv3的口罩佩戴检测算法[J].计算机工程, 2020, 46(11):12-22. WANG Y H, DING H W, LI B, et al.Mask wearing detection algorithm based on improved YOLOv3 in complex scenes[J].Computer Engineering, 2020, 46(11):12-22.(in Chinese)
[24] DAI J F, LI Y, HE K M, et al.R-FCN:object detection via region-based fully convolutional networks[C]//Proceedings of the 30th International Conference on Neural Information Processing Systems.New York, USA:ACM Press, 2016:379-387.
[25] 马俊才.面向跨视角的信号灯检测[D].北京:北京交通大学, 2020. MA J C.Cross-view-oriented traffic light detection[D].Beijing:Beijing Jiaotong University, 2020.(in Chinese)

Please choose a citation manager

Content to export