实现自动驾驶的安全性需要准确检测交通标志。针对传统方法在交通标志检测方面存在准确度不高的问题，提出一种融合RepVGG模块的改进YOLOv5的交通标志识别算法。首先通过将RepVGG模块替换原算法中的部分CBS模块，增强了特征提取能力。并在特征融合模块引入通道注意力模块（channel block attention module, CBAM），强化检测模型的抗干扰能力。最后，在网络训练过程中，使用高效交并比（efficient-IoU, EIoU）损失函数实现对目标更精确的定位，提高算法的检测精度与迭代速度。实验结果表明，改进后的YOLOv5算法迭代速度更快，在CCTSDB交通标志数据集上，其相较于原YOLOv5算法的准确率、召回率和平均准确率分别提升了4.99%、3.62%、1.73%，能够更好地应用到实践当中。

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.