Journal of Broadcast Engineering (방송공학회논문지)

The Korean Institute of Broadcast and Media Engineers (한국방송∙미디어공학회)
권호 표지
DOI QR코드

DOI QR Code

Robust Motorbike License Plate Detection and Recognition using Image Warping based on YOLOv2

YOLOv2 기반의 영상워핑을 이용한 강인한 오토바이 번호판 검출 및 인식

  • Dang, Xuan-Truong (Department of Electronics Engineering, Korea Polytechnic University) ;
  • Kim, Eung-Tae (Department of Electronics Engineering, Korea Polytechnic University)
  • 당순정 (한국산업기술대학교 전자공학부) ;
  • 김응태 (한국산업기술대학교 전자공학부)
  • Received : 2019.07.22
  • Accepted : 2019.08.30
  • Published : 2019.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

Automatic License Plate Recognition (ALPR) is a technology required for many applications such as Intelligent Transportation Systems and Video Surveillance Systems. Most of the studies have studied were about the detection and recognition of license plates on cars, and there is very little about detecting and recognizing license plates on motorbikes. In the case of a car, the license plate is located at the front or rear center of the vehicle and is a straight or slightly sloped license plate. Also, the background of the license plate is mainly monochromatic, and license plate detection and recognition process is less complicated. However since the motorbike is parked by using a kickstand, it is inclined at various angles when parked, so the process of recognizing characters on the motorbike license plate is more complicated. In this paper, we have developed a 2-stage YOLOv2 algorithm to detect the area of a license plate after detection of a motorbike area in order to improve the recognition accuracy of license plate for motorbike data set parked at various angles. In order to increase the detection rate, the size and number of the anchor boxes were adjusted according to the characteristics of the motorbike and license plate. Image warping algorithms were applied after detecting tilted license plates. As a result of simulating the license plate character recognition process, the proposed method had the recognition rate of license plate of 80.23% compared to the recognition rate of the conventional method(YOLOv2 without image warping) of 47.74%. Therefore, the proposed method can increase the recognition of tilted motorbike license plate character by using the adjustment of anchor boxes and the image warping which fit the motorbike license plate.

번호판 자동인식(ALPR: Automatic License Plate Recognition)은 지능형 교통시스템 및 비디오 감시 시스템 등 많은 응용 분야에서 필요한 기술이다. 대부분의 연구는 자동차를 대상으로 번호판 감지 및 인식을 연구하였고, 오토바이를 대상으로 번호판 감지 및 인식은 매우 적은 편이다. 자동차의 경우 번호판이 차량의 전방 또는 후방 중앙에 위치하며 번호판의 뒷배경은 주로 단색으로 덜 복잡한 편이다. 그러나 오토바이의 경우 킥 스탠드를 이용하여 세우기 때문에 주차할 때 오토바이는 다양한 각도로 기울어져 있으므로 번호판의 글자 및 숫자 인식하는 과정이 훨씬 더 복잡하다. 본 논문에서는 다양한 각도로 주차된 오토바이 데이터세트에 대하여 번호판의 문자 인식 정확도를 높이기 위하여 2-스테이지 YOLOv2 알고리즘을 사용하여 오토바이 영역을 선 검출 후 번호판 영역을 검지한다. 인식률을 높이기 위해 앵커박스의 사이즈와 개수를 오토바이 특성에 맞추어 조절하였다. 그 후 기울어진 번호판을 검출한 후 영상 워핑 알고리즘을 적용하였다. 모의실험 결과, 기존 방식의 인식률이 47.74%에 비해 제안된 방식은 80.23%의 번호판의 인식률을 얻었다. 제안된 방법은 전체적으로 오토바이 번호판 특성에 맞는 앵커박스와 이미지 워핑을 통해서 다양한 기울기의 오토바이 번호판 문자 인식을 높일 수 있었다.

Keywords

References

  1. Mei Yu, Y.D Kim, "An approach to Korean License plate recognition based on vertical edge matching", Systems, Man, and Cybernetics. IEEE International Conference, Vol.4, 8-11, 2000, https://doi.org/10.1109/icsmc.2000.884453
  2. X. He, L. Zheng and Q. Wu, "Segmentation of characters on car license plate", International Workshop on Multimedia Signal Processing, MMSP 2008, https://doi.org/10.1109/mmsp.2008.4665111
  3. R. Girshick, J. Donahue, T. Darrell and J. Malik, "Rich feature hierarchies for accurate object detection and semantic segmentation", 2014 IEEE Conference on Computer Vision and Pattern Recognition, https://doi.org/10.1109/cvpr.2014.81
  4. L. Hsi-Jian, C. Si-Yuan, W. Shen-Zheng, "Extraction and recognition of license plates of motorcycles and vehicle on highways", 17th International Conference on Pattern Recognition, 2004. ICPR,https://doi.org/10.1109/icpr.2004.1333776.
  5. G. Hsu, S. Zeng, C.Chiu and S. Chung, "A Comparison study on motorcycle license plate detection", 2015 IEEE International Conference on Multimedia & Expo Workshops(ICMEW), 2015, https://doi.org/10.1109/icmew.2015.7169772
  6. L. Dongsuk, Sook Yoon and L. Jaehwan, "Real-Time License Plate Detection Based on Faster R-CNN", KIPS Tr. Software and Data Eng. Vol.5, No.11, pp.511-520, https://doi.org/10.3745/ktsde.2016.5.11.511
  7. S. Yonetsu, Y. Iwanmato and Y. Wei Chen, "Two-Stage YOLOv2 for Accurate License-Plate Detection in Complex Scenes", 2019 IEEE International Conference on Consumer Electronics (ICCE), 2019, https://doi.org/10.1109/icce.2019.8661944
  8. S. Montazzolli and C. Jung, "Real-Time Brazilian License Plate Detection and Recognition Using Deep Convolutional Neural Networks", 2017 30th SIBGRAPI Conference on Graphics, Patterns and Images, https://doi.org/10.1109/sibgrapi.2017.14
  9. S. Montazzolli and C. Jung, "License Plate Detection and Recognition in Unconstrained Scenarios", 2018 European Conference on Computer Vision, https://doi.org/10.1007/978-3-030-01258-8_36
  10. R. Laroca, E. Severo, L. A. Zanlorensi, and L. S. Oliveira, "A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector", 2018 International Joint Conference on Neural Networks, https://doi.org/10.1109/ijcnn.2018.8489629
  11. G. R. Goncalves, S. P. Gomes da Silva, D. Menotti, W. R. Schwartz, "A Benchmark for License Plate Character Segmentation", In: Journal of Electronic Imaging, 25 (5), pp. 1-5, 2016, ISBN: 1017-9909, https://doi.org/10.1117/1.jei.25.5.053034
  12. Hsu, G.S., Chen, J.C., Chung, Y.Z, "Application-Oriented License Plate Recognition", IEEE Transactions on Vehicular Technology 62(2), https://doi.org/10.1109/tvt.2012.2226218
  13. S. Ren, K. He, R. Girshick and J. Sun., "Faster R-CNN: Towards real-time object detection with region proposal networks", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 6, pp. 1137-1149, 2017, https://doi.org/10.1109/tpami.2016.2577031
  14. J. Redmon and A.Farh., "YOLO9000: Better, Faster, Stronger", Computer Vision and Pattern Recognition(CVPR), IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 6517-6525, 2017, https://doi.org/10.1109/cvpr.2017.690