In view of the low detection accuracy, missed detection, and the wrong detection of road traffic signs in a weak light environment, a detection algorithm based on the improved YOLOX is put forward. Light weight network named Mobile Vi T Block module is adopted, meanwhile CNN is combined with Transformer to raise the network’s ability to learn local and global features of objects. By adding the adaptive feature fusion pyramid ASFF, the improved algorithm performs weighted fusion on the effective feature layers in order to accelerate the convergence speed of network training. The binary cross-entropy loss function is replaced by a Focal Loss, so as to solve the problem of inaccurate classification due to the small samples size. As shown by the experimental results, the mAP value of the improved YOLOX algorithm is increased by 2.89% than that of the YOLOX algorithm, and the number of parameters is reduced by 6.23 M. The visualization and other experiments further verify that the improved YOLOX algorithm can effectively avoid the phenomena of missing and wrong detection caused by weak light.