Deep learning-based license plate information recognition in harsh environments

To address issues of inaccuracy, missed detection, and false detection in license plate recognition algorithms, a deep learning-based recognition network for harsh environments is proposed. The FFA-Net network defogs the original image to enhance clarity. Subsequently, the YOLOX-SD detection network, incorporating the Squeeze-and-Excitation (SE) attention mechanism and deformable convolution, is designed. These enhancements reduce false and missed detections and improve adaptability to geometric deformations, thereby capturing small details on license plates effectively. The detection results are then processed by LPR-Net for information recognition. Experimental results show that the improved YOLOX-SD network achieves 99.69% accuracy, 99.62% recall, and 99.67% average precision, with LPR-Net recognition accuracy reaching 97%. Thus, the YOLOX-SD network algorithm demonstrates significant advantages for license plate detection in adverse weather conditions.