A lightweight traffic sign recognition algorithm based on YOLOv5s algorithm is proposed for the current road traffic sign model with the disadvantages of slow detection speed,large model and many parameters.Firstly,a lightweight FasterNet network is introduced,and the FasterNet Block structure in the network is fused with the C3 of the original backbone network to form a new C3Faster structure.Then the loss function of the original network is modified to MPDloU to improve the accuracy and efficiency of the bounding box regression.Finally,the efficient and lightweight SA attention mechanism is combined to improve the generalization ability and stability of the model.The experimental results on the CCTSDB 2021 dataset show that compared with the original network,the number of parameters,model size,and GFLOPs of the improved model have been reduced by 17.5%,17.5%,and 20%, respectively.Meanwhile,mAP@0.5,mAP@0.75,and mAP@0.5:0.95 have been improved by 2.3%,3.4%,and 2.4%,respectively.And comparing with other algorithms such as YOLOv3-tiny,the proposed algorithm has obvious superiority and can meet the real-time demand of mobile in various scenarios.