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Improved Method of License Plate Detection and Recognition using Synthetic Number Plate
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인조 번호판을 이용한 자동차 번호인식 성능 향상 기법

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Type
Academic journal
Author
Il-Sik Chang (서울과학기술대학교) Gooman Park (서울과학기술대학교)
Journal
The Korean Society Of Broad Engineers JOURNAL OF BROADCAST ENGINEERING Vol.26 No.4 KCI Accredited Journals
Published
2021.7
Pages
453 - 462 (10page)

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Improved Method of License Plate Detection and Recognition using Synthetic Number Plate
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A lot of license plate data is required for car number recognition. License plate data needs to be balanced from past license plates to the latest license plates. However, it is difficult to obtain data from the actual past license plate to the latest ones. In order to solve this problem, a license plate recognition study through deep learning is being conducted by creating a synthetic license plates. Since the synthetic data have differences from real data, and various data augmentation techniques are used to solve these problems. Existing data augmentation simply used methods such as brightness, rotation, affine transformation, blur, and noise. In this paper, we apply a style transformation method that transforms synthetic data into real-world data styles with data augmentation methods. In addition, real license plate data are noisy when it is captured from a distance and under the dark environment. If we simply recognize characters with input data, chances of misrecognition are high. To improve character recognition, in this paper, we applied the DeblurGANv2 method as a quality improvement method for character recognition, increasing the accuracy of license plate recognition. The method of deep learning for license plate detection and license plate number recognition used YOLO-V5. To determine the performance of the synthetic license plate data, we construct a test set by collecting our own secured license plates. License plate detection without style conversion recorded 0.614 mAP. As a result of applying the style transformation, we confirm that the license plate detection performance was improved by recording 0.679mAP. In addition, the successul detection rate without image enhancement was 0.872, and the detection rate was 0.915 after image enhancement, confirming that the performance improved.
#Synthetic license plate #Data augmentation #Style transformation #DeblurGANv2 #YOLO-V5

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Ⅰ. 서론
Ⅱ. 본론
Ⅲ. 결론
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J. Van Hulse, T. M. Khoshgoftaar, A. Napolitano, “Experimental perspectives on learning from imbalanced data,” in Proceedings of the ACM International Conference on Machine Learning, New York, pp.935-942, 2007. google schola Jae-Hyeon Lee, Sung-Man Cho, Seung-Ju Lee, Cheong-Hwa Kim, Goo-Man Park. “License Plate Recognition System Using Synthetic Data,” pp.107–115, 2020, doi:10.5573/ieie.2020.57.1.107 google schola Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, et al. “Domain-Adversarial Training of Neural Networks,” Journal of Machine Learning Research vol. 17, pp.1-35, 2016 google schola Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, et al. “Generative adversarial nets,” Adv Neural Inf Process Syst. 2014. google schola Connor Shorten, Taghi M, Khoshgoftaar, “A survey on Image Data Augmentation for Deep Learning,” Journal of Big Data 2019. google schola

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