License Plate Recognition System Based on Improved YOLOv5 and GRU

Aiming at the problem that the traditional license plate recognition method lacking of accuracy and speed, an end-to-end deep learning model for license plate location and recognition in natural scenarios was proposed. First, we added an improved channel attention mechanism to the down-sampling process of the You only look once(YOLOv5). Additionally, a location information is added in the ones to minimize the information loss from sampling, which can improve the feature extraction ability of the model. Then we reduce the number of parameters on the input side and set only one class in the YOLO layer, which improves the efficiency and accuracy of the detector for locating license plates. Finally, Gated recurrent units(GRU) + Connectionist temporal classification(CTC) was used to build the recognition network to complete the character segmentation-free recognition task of the license plate, significantly shortened the training time and improved the convergence speed and recognition accuracy of the network. The experimental results show that the average recognition precision of the license plate recognition model proposed in this paper reaches 98.98%, which is significantly better than the traditional recognition algorithm, and the recognition effect is good in complex environment with good stability and robustness.