An algorithm based on improved YOLOv5s is proposed to address the problems of small percentage of traffic signs in the image, low detection accuracy and complex surrounding environment. Firstlythe attention mechanism ECA (Efficient Channel Attention) is added to the backbone network part to enhance the feature extraction ability of the network and effectively solve the problem of complex surrounding environment;Secondly, the HASPP (Hybrid Atrous Spatial Pyramid Pooling) is proposed, which enhances the network's ability to combine context; Finally,the Neck structure in the network is modified to allow efficient fusion of high level features with underlying features while avoiding information loss across convolutional layers. Experimental results show that the improved algorithm achieves an average detection accuracy of 94.4%, a recall rate of 74.1% and an accuracy rate of 94.0% on the traffic signage dataset, which were 3.6, 2.8, and 3.4 percentage points higher than the original algorithm, respectively.