<p>Traffic sign detection is widely used in automatic driving, assisted driving, and other intelligent transportation systems, and its detection performance is closely related to rain-driving safety. In the process of traffic sign detection, there are traffic signs of different sizes with large size variations, which will have a certain impact on the detection accuracy. For the problem that the existing target detection algorithm is poor in detecting traffic signs with small sizes, low resolution, and inconspicuous features in the image, a traffic sign detection algorithm based on improved YOLOv5s is proposed. The M-ASPP module is proposed to enhance the perceptual field without reducing the feature information and increase the feature information fusion at different scales to improve the traffic sign detection accuracy. Introduce BiFPN instead of FPN to enable the fusion of deep high-level semantic information and shallow location information to improve the recognition accuracy of the algorithm. Add a small target detection head to improve the detection accuracy of small target traffic signs. Use the NWD loss function insheathe d of the IoU loss function to reduce the small-target scale sensitivity problem and improve the similarity between small-target detection. The improved traffic sign detection algorithm shows better detection performance in snowy environments, taking into account both detection accuracy and detection speed.</p>