In order to detect and recognize traffic signs on the road in real time, a traffic sign recognition model based on improved YOLOv5 is proposed to solve the problems of low recognition accuracy and serious false detection and missing detection of small traffic signs under the influence of poor lighting. First, a concat operation is added to the shallow feature layer of the YOLOv5 model, and the shallow feature information is combined with the middle feature layer and then serves as a detection head, which is conducive to the recognition efficiency of small traffic signs. Secondly, a coordinate attention mechanism is added to the YOLOv5 model to improve the efficiency of feature extraction. The Chinese traffic sign dataset TT100K is expanded, and the dark light is enhanced. Finally, the improved model detection effect is verified on the preprocessed TT100K dataset. The experimental results show that the recognition efficiency of the improved model for small and dim traffic signs is greatly improved. Compared with the results of the original YOLOv5 model trained on the expanded dataset, the accuracy of the improved YOLOv5 model in this study is improved by 1.5%, reaching 93.4%. The recall rate is increased by 6.8%, reaching 92.3%. The mAP value is increased by 5.2%, reaching 96.2%.