A deep learning based license plate number recognition method is proposed to address the issues of low accuracy and missed detection in license plate recognition under haze weather. Firstly, the AOD-Net algorithm is used to pre-process the vehicle image for defogging. Then, a license plate detection network ACG_YOLOv5s is designed based on YOLOv5 network. ACG_YOLOv5s integrates CBAM attention mechanism on the basis of YOLOv5s network to improve the model’?s anti-interference ability. An adaptive feature fusion network (ASFF) is introduced, which assigns weights to different feature layers of the network based on the weights adaptively learned by the model, thereby highlighting important feature information. The traditional convolution is replaced with Ghost convolution module and the number of parameters during network training is reduced while ensuring model performance. Finally, LPRNet is used to recognize the detected license plate images. The experimental results indicate that the improved ACG_YOLOv5s network has a license plate detection accuracy of 99.6%, LPRNet recognition accuracy of 96%, and a small memory footprint. The combination of AOD-Net algorithm and YOLO algorithm can more effectively detect license plate numbers in license plate images under haze weather.