License Plate Image Analysis Empowered By Generative Adversarial Neural Networks (GANS)

Abstract

Although the majority of existing License Plate (LP) recognition techniques have significant improvements in accuracy, they are still limited to ideal situations in which training data is correctly annotated with restricted scenarios. Moreover, images or videos are frequently used in monitoring systems that have Low Resolution (LR) quality. In this work, the problem of LP detection in digital images is addressed in the images of a naturalistic environment. Single-stage character segmentation and recognition are combined with adversarial Super-Resolution (SR) approaches to improve the quality of the LP by processing the LR images into High-Resolution (HR) images. This work proposes effective changes to the network regarding the number of layers, an activation function, and the appropriate loss regularization using Total Variation (TV) loss. The main paper contribution can be summarized into presenting YOLOv5, YOLOv6 and Faster RCNN, which are able to generate realistic super-resolution images. The experiments demonstrate that the suggested models can generate high-resolution images that improve the accuracy of the license plate recognition stage compared to other systems.