Banner Control Automation System Using YOLO and OpenCV

YOLO와 OpenCV기술을 활용한 현수막 단속 자동화 시스템 방안

  • Dukwoen Kim (Department of Smart Information and Telecommunication Engineering, Sangmyung University) ;
  • Jihoon Lee (Department of Smart Information and Telecommunication Engineering, Sangmyung University)
  • 김덕원 (상명대학교 스마트정보통신공학과) ;
  • 이지훈 (상명대학교 스마트정보통신공학과)
  • Received : 2023.11.09
  • Accepted : 2023.12.12
  • Published : 2023.12.31

Abstract

From the past to the present, banners are consistently used as effective advertising means. In the case of Korea, there are frequent situations in which hidden advertisements are installed. As a result, such hidden advertisement materials may damage urban aesthetics and moreover, incur unnecessary manpower consumption and waste of money. The proposed method classifies the detected banners into good banner and bad banner. The classification results are based on whether the relevant banners are installed in compliance with legal guidelines. In the process, YOLO and Open Computer Vision library are used to determine from various perspectives whether banners in CCTV images comply with the guidelines. YOLO is used to detect the banner area in CCTV images, and OpenCV is used to detect the color values in the area for color comparison. If a banner is detected in the video, the proposed method calculates the location of the banner and the distance from the designated bulletin to determine whether it was installed within the designated location, and then compares whether the color used in the banner is complied with local government guidelines.

Keywords

Acknowledgement

본 연구는 2023학년도 상명대학교 교내연구비를 지원받아 수행하였음. (2023-A000-0170)

References

  1. J. Jeong , "Research on Improvement of Designated Banner that is Visual Information Media in Public Spaces" Journal of the Korean Society of Design Culture, vol.23, no.2, pp.635-638, 2017. https://doi.org/10.18208/ksdc.2017.23.2.635
  2. J. Jeong and J. Yoon, "A Study on Basic Data for Information Display (Banner Hanger) Improvement & On-line System Establishment," Journal of the Korean Society of Design Culture, vol.19, no.4, pp.685-697, 2013.
  3. S. Ha, S. Jeong, Y. Jeon, and M. Jang "A Study on Vehicle License Plate Recognition System through Fake License Plate Generator in YOLOv5," Journal of The Korean Society of Industry Convergence, vol.24, no.6, pp. 699-706, 2021.
  4. H. Park, H. Jun, K. Hwang, and J. Park, "YOLO-based Cigarette Detection System Using a single Circular Bounding Box," Journal of the Korea Institute of information and Communication Engineering, vol.27, no.8, pp.913-925, 2023. https://doi.org/10.6109/jkiice.2023.27.8.913
  5. H. Ahn and Y. Lee, "Vehicle Classification and Tracking based on Deep Learning," Journal of the Semiconductor & Display Technology, vol.22, no.3, pp.161-165, 2023. https://doi.org/10.5573/JSTS.2022.22.3.161
  6. C. Park, "Performance Analysis of DNN inference using OpenCV Built in CPU and GPU Functions," Journal of the Semiconductor & Display Technology, vol.21, no.1, pp. 75-78, 2022.
  7. J. Choi, Y. Lee, and Y. Kim, "Implementation of the Panoramic System Using Feature-Based Image Stitching: Implementation of the Panoramic System Using Feature-Based Image Stitching," Journal of the Semiconductor & Display Technology, vol.16, no.2, pp. 61-65, 2017.
  8. Y. Lee and Y. Kim, "Comparison of CNN and YOLO for Object Detection," Journal of the Semiconductor & Display Technology, vol.19, no.1, pp. 85-92, 2020.
  9. Y. Lee and H. Kim, "Comparison Analysis of Deep Learning-based Image Compression Approaches," Journal of the Semiconductor & Display Technology, vol.22, no.1, pp.129-133, 2023. https://doi.org/10.33778/kcsa.2022.22.4.129
  10. D.A. Prasety, P.T. Nguyen, R. Faizullin, I. Iswanto, and E.F. Armay, "Resolving the Shortest Path Problem using the Haversine Algorithm," Journal of critical reviews, vol.7, no.1, pp.62-64, 2020.