Research on traffic sign detection based on improved YOLOv9

Mingxuan Zhang

doi:10.1117/12.3045722

9 October 2024 Research on traffic sign detection based on improved YOLOv9

Mingxuan Zhang

Proceedings Volume 13288, Fourth International Conference on Computer Graphics, Image, and Virtualization (ICCGIV 2024); 132880C (2024) https://doi.org/10.1117/12.3045722
Event: Fourth International Conference on Computer Graphics, Image, and Virtualization (ICCGIV 2024), 2024, Chengdu, China

Abstract

Aiming at the current mainstream traffic sign target detection algorithm's low accuracy, misdetection and omission of small target detection in complicated environments, this paper presents an improved traffic sign detection algorithm based on YOLOv9. AKConv is used to replace the Conv module in RepNCSPELAN4, which maintains the detection accuracy while lightening the weight. Meanwhile, Focal-EIoU Loss is proposed instead of the original regression loss function Clou Loss, this accelerates convergence and raises the accuracy of the regression by dividing the aspect ratio's loss term into the difference between the minimum outer frame's width and height and the anticipated width and height. In addition, the feature extraction capability of the network and the detection accuracy are further strengthened by adding the Convolutional Block Attention Module (CBAM) attention mechanism. On the TT100k traffic signage dataset, the improved algorithm achieves performance metrics of 92.3% and 91.5% in terms of accuracy and mAP@0.5, which are 3.1% and 3.7% higher compared to the original YOLOv9 algorithm. Moreover, the algorithm's misdetection and missed detection problems in complex environments are significantly improved, and the comprehensive detection performance is significantly higher than that of the comparison algorithms, which has greater practical value.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Mingxuan Zhang "Research on traffic sign detection based on improved YOLOv9", Proc. SPIE 13288, Fourth International Conference on Computer Graphics, Image, and Virtualization (ICCGIV 2024), 132880C (9 October 2024); https://doi.org/10.1117/12.3045722

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KEYWORDS

Object detection

Performance modeling

Detection and tracking algorithms

Target detection

Environmental sensing

Mathematical optimization

Feature extraction

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