The rapid development of autonomous driving technology has led to increasing requirements for traffic sign detection technologies. In order to solve the problems of false detection and missed detection of the YOLOv7 algorithm in identifying small targets, this study proposes a traffic sign detection model based on an attention mechanism, namely YOLOv7-PC. Firstly, a K-means++ clustering algorithm is used to cluster the traffic sign dataset to obtain anchor boxes suitable for detecting traffic signs. Secondly, the coordinate attention mechanism is introduced into the YOLOv7 backbone feature extraction network, and the horizontal and vertical information of traffic signs are embedded into the channel so that the generated feature information has the coordinate information of traffic signs, and the extraction of effective features is enhanced. Finally, the atrous spatial pyramid pooling is introduced in the enhanced feature extraction network to capture multi-scale context information of traffic signs, which ensures the resolution of small targets of traffic signs and expand the receptive field of the convolutional nucleus. Experiments on the China traffic sign detection dataset (CCTSDB) show that the proposed algorithm enhances the ability to recognize small targets. Compared with the YOLOv7 model, the proposed algorithm has an average improvement of 5.22% in mAP and 9.01% in Recall, making it an effective traffic sign detection algorithm.