Journal of South China University of Technology(Natural Science Edition) ›› 2026, Vol. 54 ›› Issue (3): 65-78.doi: 10.12141/j.issn.1000-565X.250092

• Intelligent Transportation System • Previous Articles     Next Articles

Small Traffic Sign Recognition Algorithm Based on CGT-YOLO

XING Yan1,2  GUO Sihao1  ZHANG Zhen2,3  PAN Xiaodong2,3  AN Dong1,4   

  1. 1. Shenyang Jianzhu University, Shenyang 110168, Liaoning, China;

    2. National Engineering Research Center for Road Traffic Safety Control Technology, Shenyang 110168, Liaoning, China;

    3. Public Security Bureau of Shenyang Municipality, Traffic Police Bureau, Shenyang 110168, Liaoning, China;

    4. Shenyang Cambrian Transportation Technology Co., Ltd, Shenyang 110168, Liaoning, China

  • Online:2026-03-25 Published:2025-10-31

Abstract:

To address the issue of decreased recognition accuracy caused by false and missed detections of small target traffic signs, a small traffic sign recognition algorithm based on CGT-YOLO is proposed. First, the Context Enhancement Module is introduced to replace the SPPF module in the YOLOv5s network, enhancing the feature representation of small traffic signs through convolutions with varying dilation rates. Next, the Global Attention Module is added after the Concat module in the YOLOv5s backbone network. The GAM extracts the CAM-enhanced features and further strengthens the global interaction between channels and spatial dimensions using 3D permutation, multi-layer perceptron, and convolutional spatial attention. This process highlights the features of small target traffic signs and mitigates the negative effects of complex backgrounds and long distances. Finally, the TSC decoupling head is utilized to separate the features for classification and localization tasks. This module generates semantically rich, low-resolution classification feature maps and high-resolution localization maps containing boundary information, effectively resolving the feature conflicts between the two tasks. Experimental results show that the improved model has improved in all indicators: the miss rate and false positive rate decreased by 12.1% and 11.6%, respectively, and the mAP(0.5:0.95) increased by 2.6%. These improvements effectively reduce false positives and false negatives, significantly, enhancing the recognition accuracy of small traffic signs.

Key words: small target recognition, traffic sign recognition, dilated convolution, attention mechanism, decoupling head