In order to solve the problems of slow license plate detection and recognition models and low detection accuracy in complex environments,an end-to-end license plate recognition algorithm that can detect and recognize license plates with high ac-curacy in complex environments is proposed.In the feature layer output process of the YOLOv7 network layer,the CBAM channel attention mechanism is added to improve the feature extraction capability of the model;the improved YOLOv7 algorithm is used to detect license plates in complex environments,and the detected license plate areas are pre-processed.In the processing operation,the processed license plate is input into the improved CNN recognition model for character recognition.Experimental results show that the average detection accuracy of the YOLOv7 detection model after adding the attention mechanism reaches 87.5%,and the recognition accuracy of the improved recognition model reaches 97.16%,which is significantly better than the traditional license plate recognition technology,and the recognition effect is good in complex environments,has practical application value.
关键词
深度学习/目标检测/车牌识别/注意力机制/YOLOv7
Key words
deep learning/target detection/license plate recognition/attention mechanism/YOLOv7
万方数据