In view of the problem of slow detection speed and large network model of traffic signs in the process of automatic driving,an improved YOLOv5s-Ghost network model is proposed to identify traffic signs.Under the framework of 3× 3 computing core Ghost Net model,the Ghost Bottleneck module is constructed by two consecutive Ghost modules,which replace all the Bottleneck module of C3 modules and combine with cross-stage position network (CSPNet) to generate Ghost Bottleneck CSP module.The best netw o rk model is obtained by comparing the experimental data by adjusting the number of Ghost Bottleneck modules added to each module.The original network and the n ew network are respectively used to train TT100K data set.Compared experimenta l data show that the detection accuracy of the YOLOv5s-Ghost model is 95.1%,th e detection speed reaches 52.6 FPS,and the model size is compressed by 69.3%,whi ch improves the detection speed of the network while ensuring the original detec tion accuracy.