YOLO Based Recognition Method for Automatic License Plate Recognition

Abstract

Over the past two decades, the number of vehicles have increased intensely. With this increase, it is gradually more difficult to track each vehicle for law enforcement and traffic management purposes. Automatic License Plate Recognition (ALPR) is a popular surveillance system that captures vehicle images and identifies their license plate numbers and became an important research topic of this era. In our study, AOLP dataset is used for the license plate recognition. Keeping the strategy of multi task learning for character string recognition we employed YOLOv3 for the recognition and CRNN for classification for our proposed method. For evaluation, we allocated 40% images to the training set, 20% to the validation set and 40% to the test set. For the test set evaluation we choose lower threshold i.e. 0.125 achieving 99.82 recall. Our proposed method achieved 86% recognition rate with recognizing 88% three letter and 99% of four letter plates. In the end while using temporal redundancy, final recognition rate is significantly improved i.e. 96%. Our proposed method improves recognition rates from 93.58% to 96.1% having outperforming Sighthound and OpenALPR by 9% and 4.9%, respectively.