Minimizing Illumination Effect in License Plate Recognition

Abstract

The intelligent transportation system is a key technology for efficient traffic control that has been applied in various fields. The existing intelligent transportation system detects the license plates of vehicles mainly through image feature analysis technology by using image-processing techniques. While this method has the advantage of quickly recognizing license plates by simple computing when the environment for recognizing license plate images is favourable, its accuracy is significantly compromised by various environmental changes. This study proposes a method using Faster region-based CNN (R-CNN) and denoising autoencoder technology to improve the recognition performance for tilted and broken plates and false recognition caused by illumination effects in the access control automation system installed at construction sites where these poor conditions frequently occur. This study investigated 3,000 images collected from actual construction sites, comparing the proposed method with the existing Faster R-CNN for license plates affected by various illumination environments, and found an accuracy improvement of more than 30%.