Improved traffic sign detection algorithm based on improved YOLOv8s

Authors

  • Xin He
  • Tianyu Li
  • Yiqi Yang

DOI:

https://doi.org/10.54097/

Keywords:

Traffic sign, Target detection, Attention mechanism, IoU

Abstract

Aiming at the problem of low accuracy in current traffic sign detection, a new traffic sign detection model YOLOV8-CO with high accuracy was proposed based on YOLOv8 algorithm in this paper. CCMA attention mechanism was introduced and C2f module was replaced by C2fO module. The global average pooling strategy was used to replace the traditional fully connected layer, and the corresponding relationship between feature maps and categories was forced to better conform to the convolutional structure. CIoU and YOLOv8 original loss function were selected to calculate the loss value and train the model. The detection accuracy of traffic signs is improved effectively and overfitting is avoided. TT100K data set and COCO data set were used to verify the validity of the algorithm. The experimental results show that on TT100K, mAP@0.5 increases by 2.2%, mAP@0.7 increases by 2.4%, mAP@0.5:0.95 increases by 1.6%; Improved 0.7% on the COCO dataset, mAP@0.7 improved 0.6%, and mAP@0.5:0.95 improved 0.7%.

References

Feng D, Haase-Schütz C, Rosenbaum L, et al. Deep multi-modal object detection and semantic segmentation for autonomous driving: Datasets, methods, and challenges[J]. IEEE Transactions on Intelligent Transportation Systems, 2020, 22(3):

Wu Xiaohui, Tian Qichuan. Review of traffic sign recognition methods [J]. Computer Engineering and Applications, 2020, 56(10): 20-26.

Arcos-García Á, Alvarez-Garcia J A, Soria-Morillo L M. Evaluation of deep neural networks for traffic sign detection systems[J]. Neurocomputing, 2018, 316: 332-344.

Li D. Research on Traffic Sign Detection and Recognition Method[J]. World Scientific Research Journal, 2022, 8(3): 462-467.

Temel D, Alshawi T, Chen M H, et al. Challenging environments for traffic sign detection: Reliability assessment under inclement conditions[J]. arXiv preprint arXiv:1902.06857, 2019.

Gui Xiangquan, Liu Shiqing, Li Li, et al. Pedestrian detection algorithm in scenic spots based on improved YOLOv8 [J]. 2023.

Qiu Tianheng, Wang Ling, Wang Peng. Research on target detection algorithm based on improved YOLOv5 [J]. Computer Engineering and Applications, 2022, 58(13): 63-73.

SUN Chuanmeng, WANG Yanping, WANG Chong, et al. Coal-rock interface recognition method integrating improved YOLOv3 and cubic spline interpolation[J]. Journal of Mining andRock Control Engineering,2022,4(01):81-90.

W.H. Li, B. Zhou, B. Hu, Z.H. Zhang. Occluded face detection based on lightweight network[J]. Journal of South -Central Minzu University (Natural Science Edition),2022, 41(03):339-346. (in Chinese)

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.

Lin, Min, Qiang Chen, and Shuicheng Yan. "Network in network." arXiv preprint arXiv:1312.4400 (2013).

Hu, Mu, et al. "Online convolutional re-parameterization." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022.

Cao Chengshuo, and Yuan Jie. "Mask wearing detection method based on YOLO-Mask algorithm." Laser & Optoelectronics Progress 58.8 (2021): 0810019.

WANG Yicheng,ZHANG Guoliang,ZHANG Zijie. Sm-all target detection method based on improved YOLOv5[J]. Computer and Modernization, 2023(05):100-105. (in Chinese)

Zhu, Zhe, et al. "Traffic-sign detection and classification in the wild." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.

Downloads

Published

30-03-2024

Issue

Section

Articles

How to Cite

He, X., Li, T., & Yang, Y. (2024). Improved traffic sign detection algorithm based on improved YOLOv8s. Journal of Computing and Electronic Information Management, 12(2), 38-45. https://doi.org/10.54097/

Similar Articles

1-10 of 65

You may also start an advanced similarity search for this article.