Journal of Advanced Navigation Technology (한국항행학회논문지)

The Korean Navigation Institute (한국항행학회)
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Implementing Parking Zone Management System for Disabled based on Deep Learning using Cloud Platform

클라우드 플랫폼을 이용한 딥러닝 기반 장애인 주차구역 관리 시스템 구현

  • 황주훈 (가천대학교 에너지 IT학과) ;
  • 김창복 (가천대학교 에너지 IT학과)
  • Received : 2021.03.16
  • Accepted : 2021.04.29
  • Published : 2021.04.30
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Abstract

This study proposed a system that manages parking spaces for the disabled, this will lead to the promotion of welfare for those who are disabled by using deep learning and cloud platforms. Deep learning used you only look once (YOLO) for license plate detection concerning car images in parking areas, and convolutional neural network (CNN) was used for license plate character recognition from extracted numbers and text images. This system can be managed in real time, and it has been simplified so that it can be managed only with video. In addition, it is recognized and accurate by increasing the recognition rate of Korean characters compared to the existing optical character recognition (OCR), and it has the advantage of scalability in the management area by enabling parking management but only if closed circuit television (CCTV) is installed. This system requires a study to increase the accurate license plate recognition rate. This is an important factor, and a continuous study on the processing speed problem to execute YOLO and CNN algorithms in a somewhat low performance raspberry environment.

본 연구는 딥러닝과 클라우드 플랫폼을 이용하여 장애인 복지 증진을 위한 장애인 주차 공간을 관리하는 시스템을 제안하였다. 딥러닝은 주차 영역의 자동차 영상에서 번호판 검출을 위하여 YOLO (you only look once)를 사용하였으며, 추출된 숫자 및 문자 영상에서 번호판 문자 인식을 위하여 CNN (convolutional neural network)을 사용하였다. 본 시스템은 실시간 관리가 가능하고, 동영상만으로 관리할 수 있도록 간소화하였다. 또한 기존 OCR (optical character recognition)보다 한글 문자 인식률을 높임으로서 안정성 및 정확성이 있으며, CCTV (closed circuit television)만 설치하면 주차관리가 가능하도록 함으로서 관리 영역의 확장성의 특장점을 가진다. 본 시스템은 기징 중요한 요소인 정확한 번호판 인식률을 높이는 연구와 다소 성능이 낮은 라즈베리 파이 환경에서 YOLO와 CNN 알고리즘 등을 실행하기 위한 처리속도 문재에 대한 지속적 연구가 필요하다.

Keywords

References

  1. J. M. Jang and T. H. Gim, "A study on parking policy approach based on the social costs of illegal parking," Journal of Transport Research, Vol. 24, No. 3, pp. 45-59, Sept. 2017.
  2. S. J. and G. M. Park, "Proposal for license plate recognition ssing synthetic data and vehicle type recognition system," Journal of Broadcast Engineering, Vol. 25, No. 5, pp. 776-788, Sept. 2020. https://doi.org/10.5909/JBE.2020.25.5.776
  3. J. H. Kim and J. H. Lim, "License plate detection and recognition algorithmusing deep learning," Journal of Korean Electrical and Electronics Engineers, Vol. 23, No. 2, pp. 642-651, June 2019.
  4. J. T. Koo, M. J. Park, J. Y. Yang, J. H. Lee, Y. S. and Shin, Y. S. Song, "A study on the development of the Client-server-based Real-time handicapped parking system using deep learning model," in Proceedings of The Korean Institute of Information Scientists and Engineers, Pyeongchang: Korea, pp. 1483-1485, 2019.
  5. X. Zhou, C. Yao, H. Wen, Y. Wang, S. Zhou, W. He, and J.n Liang, "EAST : an efficient and accurate scene text detector," in Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, Honolulu: HI, pp. 5551-5560, 2017.
  6. S. Kumari, L. Gupta, and P. Gupta, "Automatic license plate recognition using OpenCV and neural network," International Journal of Computer Science Trends and Technology, Vol. 5, No. 3, pp. 114-118, 2017.
  7. X. T. Dang and E. T. Kim, "Robust motorbike license plate detection and recognition using image warping based on YOLOv2," Journal of Broad Engineers, Vol. 24, No. 5, pp. 713-725, 2019.
  8. J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, "You Only Look Once: Unified, Real-time object detection," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas: NV, pp. 779-788, 2016.
  9. R. Joseph and A. Farhadi. "YOLO9000 : better, faster, stronger," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu: HI, pp. 7263-7271, 2017.
  10. G. Ross. "Fast r-cnn," in Proceedings of the IEEE International Conference on Computer Vision, Santiago: Chile, pp. 1440-1448, 2015.
  11. 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, 2016. https://doi.org/10.1109/TPAMI.2016.2577031