Research on Dynamic License Plate Recognition Algorithm Based on Deep Learning
[Purposes]In response to the relatively weak license plate recognition capability of current li-cense plate recognition systems under complex scene conditions,an improved recognition algorithm for license plates in complex scenes based on YOLOv7 and LPRNet is proposed.[Methods]First,a global attention template(GAM)is introduced into the YOLOv7 head network to enhance attention to the li-cense plate area,thereby improving the accurate positioning of license plates.Secondly,the four-point perspective transformation method is employed to correct deformed license plate images,providing nor-malized and stable inputs for effective subsequent license plate recognition.Finally,the LPRNet model is applied to achieve end-to-end recognition of the processed license plate images,completing the ex-traction and identification of license plate information.[Findings]Experiments conducted on the CCPD dataset demonstrate a license plate detection accuracy of 99%and a license plate recognition accuracy of 99.19%.[Conclusions]Research results indicate that,despite facing challenges in complex scenes,the proposed license plate recognition algorithm still maintains excellent performance in license plate detec-tion and recognition.
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