Improved YOLOv5 based on repVGG and ECA prediction head for real-time traffic sign recognition

Jiageng Qiao

doi:10.1117/12.3048393

3 October 2024 Improved YOLOv5 based on repVGG and ECA prediction head for real-time traffic sign recognition

Jiageng Qiao

Author Affiliations +

Proceedings Volume 13272, Fifth International Conference on Computer Vision and Data Mining (ICCVDM 2024); 132722H (2024) https://doi.org/10.1117/12.3048393
Event: 5th International Conference on Computer Vision and Data Mining (ICCVDM 2024), 2024, Changchun, China

Abstract

Traffic sign recognition is an important task in driverless technology. Because the traffic signs are located in a complex environment and the target scale changes greatly, it brings a burden to the network optimization. In reality, it is usually difficult for common models to achieve good detection accuracy while ensuring the detection speed. In order to solve this problem, we introduce the structural re-parameterization technique on the basis of YOLOv5, try to take advantage of the ad-vantages of high performance of multi-branch model and fast reasoning of single-channel model at the same time, and embed the effective channel attachment (ECA) module on the prediction head to find the attention area in complex scenes. Extensive experiments on Tsinghua-Tent 100K (TT100K) dataset show that our improved model can give consideration to recognition speed and accuracy with practical application value

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

Citation Download Citation

Jiageng Qiao "Improved YOLOv5 based on repVGG and ECA prediction head for real-time traffic sign recognition", Proc. SPIE 13272, Fifth International Conference on Computer Vision and Data Mining (ICCVDM 2024), 132722H (3 October 2024); https://doi.org/10.1117/12.3048393

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KEYWORDS

Convolution

Performance modeling

Object detection

Head

Target recognition

Education and training

Data modeling

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