License Plate Recognition and Detection Technology in Complex Scenes Based on Mf-RepUnet and Improved Deep Residual Recognition Modules

In recent years, the rapid development of Intelligent Transportation Systems (ITS) has made License Plate Recognition (LPR) technology crucial for automatic monitoring, traffic management, and crime prevention. However, challenges such as variations in lighting, perspective distortion, and background interference in complex environments significantly hinder the effectiveness of traditional license plate detection algorithms. To tackle these issues, this paper proposes a license plate recognition system based on a multi-scale feature extraction framework (Mf RepUnet), an enhanced deep residual network (Residual Neural Network, ResNet), and a CycleGAN (Generative Adversarial Network) strategy.