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2026 , Vol. 31 >Issue 2: 319 - 333

DOI: https://doi.org/10.1007/s12204-024-2712-5

Automation & Computer Technologies

Traffic Light Recognition Based on Improved YOLOv5l

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  • College of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin 132022, Jilin, China

Received date: 2023-04-26

  Accepted date: 2023-08-16

  Online published: 2024-02-20

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Abstract

Accurate recognition of traffic lights is essential for ensuring the safety of passengers and pedestrians, especially in the context of self-driving car technology. However, traffic lights present challenges due to their small size and limited recognition accuracy. This paper proposes an enhanced version of the YOLOv5l algorithm specifically designed for traffic light recognition. First, the K-means++ clustering algorithm is employed to generate the prior frame. Second, the SiLU activation function in the basic convolution module is replaced with the adaptive Meta-ACONC activation function, significantly improving the model’s detection accuracy. Third, the coordinate attention mechanism is integrated into the trunk feature extraction network to incorporate coordinate information into the channel, thereby enhancing the network’s sensitivity to small target positions and mitigating the ambiguity caused by increased network depth. Finally, the network’s detection scale is improved by removing the original 20 × 20 large target detection head, leading to an improved accuracy and speed for detecting small targets. The proposed approach is evaluated on self-created traffic light datasets, and compared with the original YOLOv5l model; the improved YOLOv5l model achieves a 7.1% increase in mAP@0.5, reaching 83.3%, effectively meeting the requirements for traffic light detection and recognition.

Cite this article

Dong Ruyi, Shi Cong . Traffic Light Recognition Based on Improved YOLOv5l[J]. Journal of Shanghai Jiaotong University(Science), 2026 , 31(2) : 319 -333 . DOI: 10.1007/s12204-024-2712-5

References

[1] JIN T, WANG C X, WANG B, et al. Traffic lights recognition based on concatenated filtering method [J]. Journal of Shanghai Jiaotong University, 2012, 46(9): 1355-1360 (in Chinese).

[2] GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation [C]//2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus: IEEE, 2014: 580-587.

[3] GIRSHICK R. Fast R-CNN [C]//2015 IEEE International Conference on Computer Vision. Santiago: IEEE, 2015: 1440-1448.

[4] 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.

[5] REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: Unified, real-time object detection [C]//2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 779-788.

[6] REDMON J, FARHADI A. YOLO9000: better, faster, stronger [C]//2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2017: 6517-6525.

[7] REDMON J, FARHADI A. YOLOv3: An incremental improvement [DB/OL]. (2018-04-08). https://arxiv.org/abs/1804.02767

[8] BOCHKOVSKIY A, WANG C Y, LIAO H Y M. YOLOv4: Optimal speed and accuracy of object detection [DB/OL]. (2020-04-23). http://arxiv.org/abs/2004.10934

[9] LIN T Y, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 42(2): 318-327.

[10] LIU W, ANGUELOV D, ERHAN D, et al. SSD: single shot MultiBox detector[M]// Computer vision – ECCV 2016. Cham: Springer, 2016: 21-37.

[11] LI D P, REN X M, YAN N N. Real-time detection of insulator drop string based on UAV aerial photography [J]. Journal of Shanghai Jiao Tong University, 2022, 56(8): 994-1003 (in Chinese).

[12] MAO T. Traffic signal detection algorithm based on YOLO [J]. Digital Technology & Application, 2021, 39(6): 97-99 (in Chinese).

[13] Guo H X. Object detection in traffic scene based on  deep learning [D]. Xuzhou: China University of Mining and Technology, 2021 (in Chinese).

[14] DENG T M, TAN S Q, PU L Z. Traffic light recognition method based on improved YOLOv5s [J]. Computer Engineering, 2022, 48(9): 55-62 (in Chinese).

[15] ZHU K, CHEN C F. Traffic sign recognition under fog weather based on YOLOv5 [J]. Electronic Measurement Technology, 2023, 46(8): 31-37 (in Chinese).

[16] LI W, ZHANG G, CUI L, et al. Lightweight traffic sign recognition model based on coordinate attention[J]. Journal of Computer Applications, 2023, 43(2): 608-614 (in Chinese).

[17] 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 (in Chinese).

[18] QIAN W, WANG G Z, LI G P. Improved YOLOv5 traffic light real-time detection robust algorithm [J]. Journal of Frontiers of Computer Science & Technologyy, 2022, 16(1): 231-241 (in Chinese).


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