关键词: 深度学习；目标检测；车牌识别；YOLOv5；注意力机制；长短期记忆网络

Abstract: Aiming at the problems of low efficiency, poor robustness and low recognition precision of existing license plate recognition systems, this paper proposed an end-to-end deep learning model for license plate detection and recognition in a high-precision real-time environment. First, adds an improved channel attention mechanism to the down-sampling process of the YOLOv5 network layer, which incorporates location information to reduce the information loss caused by sampling and improve the feature extraction capability of the model. Secondly, uses the combination of LSTM+ CTC to construct the recognition network to complete the recognition of license plate without character segmentation, which greatly reduces the training cycle and improves the recognition accuracy and efficiency of the model. This paper has conducted extensive experiments on chinese city parking dataset (CCPD), and the results show that the average recognition accuracy of the improved model of license plate recognition proposed in this paper reaches 98.03%, which is significantly better than the traditional license plate recognition technology, and the recognition effect is good in complex environment with strong robustness.

Key words: Deep learning; Target detection; License plate recognition; YOLOv5; Attention mechanism; Long short-term memory