The invention discloses a method for improving the traffic sign recognition precision in extreme weather and environment, which is based on a YoV5 target detection model, integrates a focusing module, a cross-stage local fusion module and a spatial pyramid pooling structure, and can better extract feature map information from local features for traffic sign images with poor light, and the feature map more accurately expresses the image. For a small number of training data, the expressions of the traffic signs in different environments are simulated by using Gaussian noise, adding salt and pepper noise, reducing brightness, sharpening an image, reducing the size and the like in proportion, and the traffic signs are copied to a target-free picture by using a copying-pasting method, so that a data set is greatly enriched. By using the method provided by the invention, different image modes under different resolutions can be captured more easily, and the features of the target can be extracted and fused to the greatest extent; and meanwhile, convergence is quicker and more accurate, fewer positioning errors exist, and more accurate prediction is generated.