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License Plate Recognition System Using Synthetic Data
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인조 데이터를 활용한 차량 번호판 인식 시스템

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Type
Academic journal
Author
Jae-Hyeon Lee (서울과학기술대학교) Sung-Man Cho (서울과학기술대학교) Seung-Ju Lee (서울과학기술대학교) Cheong-Hwa Kim (서울과학기술대학교) Goo-Man Park (서울과학기술대학교)
Journal
The Institute of Electronics and Information Engineers Journal of the Institute of Electronics and Information Engineers Vol.57 No.1(Wn.506) KCI Excellent Accredited Journal
Published
2020.1
Pages
107 - 115 (9page)
DOI
10.5573/ieie.2020.57.1.107

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License Plate Recognition System Using Synthetic Data
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Research history (3)

2020

A study on license plate recognition based on deep learning using synthetic data
이재현 미디어IT공학과 2020.01 Thesis
License Plate Recognition System Using Synthetic Data
Jae-Hyeon Lee ,  Sung-Man Cho ,  Seung-Ju Lee and others 3 Journal of the Institute of Electronics and Information Engineers 2020.01 Academic journal

2019

인조 번호판 데이터를 활용한 YOLO v3 기반 번호판 인식 시스템 구현
이재현 ,  조성만 ,  김청화 and others 1 대한전자공학회 학술대회 2019.06 Proceeding
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Abstract· Keywords

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In deep learning, training data can determine the performance of the network and increase the performance of the network from a diverse training data. However, gathering and labeling training data requires significant time and personnel. Likewise, various types of license plates and text labeling are required for deep learning based license plate character recognition. In this paper, we developed an synthetic license plate data generation program to solve the problem of time-consuming data collection and labeling. The proposed license plate recognition system is an image taken in a drone environment, which makes it difficult to recognize the license plate character. In order to recognize the license plate character in the adverse environment, we applied the methods such as scale transform, brightness conversion, rotation, background addition, noise and blur addition to the synthetic license plate to generate the training data in the harsh environment. To evaluate the training results of the synthetic license plate data, we collected 1,000 real license plates and constructed a test set. As a result, the total character recognition accuracy was 85% and the individual character recognition accuracy was 94% using 1.5M synthetic license plate data without the real license plate data. After that, 88, 96% accuracy was recorded through the post-processing algorithm.
#YOLO #LPR(License Plate Recognition) #Synthetic Data #Object Detection

Contents

요약
Abstract
Ⅰ. 서론
Ⅱ. 관련 연구
Ⅲ. 인조 번호판 데이터 생성
Ⅳ. 차량 번호판 인시 시스템
Ⅴ. 실험
Ⅵ. 결론
REFERENCES

References (24)

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L. Deng / 2012 / The Mnist Database of Handwritten Digit Images for Machine Learning Research / Signal Processing Magazine 29(6):141~142 google schola A. Krizhevsky / 2009 / Learning multiple layers of features from tiny images google schola O. Russakovsky / 2014 / Imagenet large scale visual recognition challenge / arXiv:1409.0575 google schola T. -Y. Lin / 2014 / Microsoft COCO: Common objects in context / ECCV google schola Rayson Laroca / 2018 / A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector / arXiv:1802.09567v6 google schola

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