Abstract
With the development and continuous iteration of digital transformation and artificial intelligence technology, the license plate detection and recognition system based on traditional machine vision and the current deep learning-based license plate recognition system for China is unable to meet the needs of rapid and accurate real-time recognition and recognition in complex environments. This paper designs and integrates a set of license plate detection and recognition system based on YOLOv7, STN and LPRNet models, which can recognize Chinese license plates quickly and accurately in real time, and has good robustness in complex environment. Its average accuracy in complex environments reached 96.1%, indicating that the system has a better effect than the traditional license plate detection and recognition system.
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