In order to achieve the safety of autonomous driving, it is crucial to accurately detect traffic signs. Addressing the issue of low accuracy in traditional methods for traffic sign detection, a modified YOLOv5 algorithm incorporating the RepVGG module for traffic sign recognition was proposed. Firstly, by replacing certain CBS modules in the original algorithm with the RepVGG module, the feature extraction capability was enhanced. Additionally, the channel block attention module (CBAM) attention mechanism was introduced in the feature fusion module to strengthen the detection model's resistance to interference. Finally, during network training, the efficient-IoU (EIoU) loss function was utilized to achieve more precise target positioning, thereby improving the algorithm's detection accuracy and iteration speed. Experimental results demonstrate that the improved YOLOv5 algorithm exhibits faster iteration speed. On the CCTSDB traffic sign dataset, it achieves improvements of 4.99% in accuracy, 3.62% in recall rate, and 1.73% in average accuracy compared to the original YOLOv5 algorithm, making it more suitable for practical applications.