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학술지 License Plate Image Generation using Generative Adversarial Networks for End-To-End License Plate Character Recognition froma Small Set of Real Images
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저자
한병길, 이종택, 임길택, 최두현
발행일
202004
출처
Applied Sciences, v.10 no.8, pp.1-16
ISSN
2076-3417
출판사
MDPI
DOI
https://dx.doi.org/10.3390/APP10082780
협약과제
20ZD1100, 대경권 지역산업 기반 ICT 융합기술 고도화 지원사업, 문기영
초록
License Plate Character Recognition (LPCR) is a technology for reading vehicle registration plates using optical character recognition from images and videos, and it has a long history due to its usefulness. While LPCR has been significantly improved with the advance of deep learning, training deep networks for LPCR module requires a large number of license plate (LP) images and their annotations. Unlike other public datasets of vehicle information, each LP has a unique combination of characters and numbers depending on the country or the region. Therefore, collecting a sufficient number of LP images is extremely difficult for normal research. In this paper, we propose LP-GAN, an LP image generation method, by applying an ensemble of generative adversarial networks (GAN), and we also propose a modified lightweight YOLOv2 model for an efficient end-to-end LPCR module. With only 159 real LP images available online, thousands of synthetic LP images were generated by using LP-GAN. The generated images not only looked similar to real ones, but they were also shown to be effective for training the LPCR module. As a result of performance tests with 22,117 real LP images, the LPCR module trained with only the generated synthetic dataset achieved 98.72% overall accuracy, which is comparable to that of training with a real LP image dataset. In addition, we improved the processing speed of LPCR about 1.7 times faster than that of the original YOLOv2 model by using the proposed lightweight model.
키워드
ALPR, End-to-end recognition, Ensemble data, GAN, License plate image generation, Segmentation-free
KSP 제안 키워드
End to End(E2E), Image datasets, Image generation, License plate character recognition, License plate image, Lightweight model, Optical character recognition, Overall accuracy, Performance Test, Processing speed, Public Datasets
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