Traffic sign recognition is a key part of autonomous driving technology. Given the problems of small targets and low recognition accuracy of traffic signs in road scenes, an improved YOLOv5 algorithm is proposed. First, the global attention mechanism (GAM) is introduced into the YOLOv5 model to improve the network’s ability to capture traffic sign features of different scales. Second, the GIoU loss function used in the YOLOv5 algorithm is replaced with the CIoU loss function which is more regressive to optimize the model and improve the recognition accuracy of traffic signs. Finally, the training is carried out on the Tsinghua-Tencent 100K dataset. The experimental results show that the average accuracy of the improved YOLOv5 algorithm for traffic sign recognition is 93.00%, which is 5.72% higher than that of the original one, indicating that the improved algorithm has better recognition performance.