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http://dx.doi.org/10.30693/SMJ.2020.9.4.134

Improved Method of License Plate Detection and Recognition Facilitated by Fast Super-Resolution GAN  

Min, Dongwook (한국교통대학교 소프트웨어학과)
Lim, Hyunseok (한국교통대학교 소프트웨어학과 대학원)
Gwak, Jeonghwan (한국교통대학교 소프트웨어학과)
Publication Information
Smart Media Journal / v.9, no.4, 2020 , pp. 134-143 More about this Journal
Abstract
Vehicle License Plate Recognition is one of the approaches for transportation and traffic safety networks, such as traffic control, speed limit enforcement and runaway vehicle tracking. Although it has been studied for decades, it is attracting more and more attention due to the recent development of deep learning and improved performance. Also, it is largely divided into license plate detection and recognition. In this study, experiments were conducted to improve license plate detection performance by utilizing various object detection methods and WPOD-Net(Warped Planar Object Detection Network) model. The accuracy was improved by selecting the method of detecting the vehicle(s) and then detecting the license plate(s) instead of the conventional method of detecting the license plate using the object detection model. In particular, the final performance was improved through the process of removing noise existing in the image by using the Fast-SRGAN model, one of the Super-Resolution methods. As a result, this experiment showed the performance has improved an average of 4.34% from 92.38% to 96.72% compared to previous studies.
Keywords
Object Detection; Super-Resolution; Generative Adversarial Network; License Plate Detection; License Plate Recognition;
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Times Cited By KSCI : 2  (Citation Analysis)
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