Research on Algorithm for License Plate Detection in Complex Scenarios Based on Artificial Intelligence

With the advancement of transportation technology, the management of vehicles on the roadside has become a pressing issue for traffic management and parking authorities. Consequently, non-fixed-scene license plate detection has become a focal point for academic and industrial research and solutions. In response to the inadequacies of existing license plate detection algorithms in complex environments, such as different locations and angles, the papper employed the PaddleOcrV4 as license plate detection model based on deep learning. This model utilized PaddlePaddle as its foundational framework and replaces MobileNetv3 with PP-LCNetV3 in the license plate detection network to reduce interference from complex environmental backgrounds. In the license plate recognition network, PP-HGNet served as the backbone network, effectively improving detection accuracy. This paper primarily designed a solution for license plate detection and text recognition based on OCR technology. Experimental results indicated that the algorithm presented in this paper outperforms PaddleOcrV3 and PaddleOcrV3 in terms of detection accuracy, achieving a precision rate of 96% on the test dataset.