Abstract: In recent years, autonomous driving has begun to come into people’s sight. For autonomous driving, traffic sign detection in fuzzy light scene is an extremely important part. At present, YOLO v4 algorithm has been widely used in target detection, although its detection accuracy is greatly improved compared with other versions, but it has not reached the expected accuracy. In order to further improve the accuracy of detecting traffic signs, this article makes certain improvements on the basis of the original YOLO v4 and combines it with MSRCR image enhancement processing. Firstly, the original training images are enhanced by MSRCR algorithm, and it is used as the training set image of target detection. This article uses Darknet-53’s YOLO v4 network, labeles BelgiumTS traffic signal data set by labelImg, and uses the improved K-means++ clustering algorithm to determine the  priori box and specific parameters, and improves the path aggregation network（PANet） structure and loss function to train the data set. Experimental results show that compared with the original YOLO v4 algorithm, the improved algorithm has an average accuracy increase of 1.86 percentage points.

Key words: traffic sign detection, MSRCR algorithm, YOLO v4 algorithm, K-means++ clustering algorithm, Loss function； PANet