The Journal of The Korea Institute of Intelligent Transport Systems (한국ITS학회 논문지)

The Korea Institute of Intelligent Transport Systems (한국ITS학회)
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Freeway Bus-Only Lane Enforcement System Using Infrared Image Processing Technique

적외선 영상검지 기술을 활용한 고속도로 버스전용차로 단속시스템 개발

  • Jang, Jinhwan (Dept. of Highway and Transport, Korea Institute of Civil Engineering and Building Technology)
  • 장진환 (한국건설기술연구원 도로교통연구본부)
  • Received : 2022.09.27
  • Accepted : 2022.10.12
  • Published : 2022.10.31
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Abstract

An automatic freeway bus-only lane enforcement system was developed and assessed in a real-world environment. Observation of a bus-only lane on the Youngdong freeway, South Korea, revealed that approximately 99% of the vehicles violated the high-occupancy vehicle (HOV) lane regulation. However, the current enforcement by the police not only exhibits a low enforcement rate, but also induces unnecessary safety and delay concerns. Since vehicles with six passengers or higher are permitted to enter freeway bus-only lanes, identifying the number of passengers in a vehicle is a core technology required for a freeway bus-only lane enforcement system. To that end, infrared cameras and the You Only Look Once (YOLOv5) deep learning algorithm were utilized. For assessment of the performance of the developed system, two environments, including a controlled test-bed and a real-world freeway, were used. As a result, the performances under the test-bed and the real-world environments exhibited 7% and 8% errors, respectively, indicating satisfactory outcomes. The developed system would contribute to an efficient freeway bus-only lane operations as well as eliminate safety and delay concerns caused by the current manual enforcement procedures.

본 연구에서는 고속도로 버스전용차로 단속시스템을 개발하여 현장 성능평가를 수행하였다. 영동고속도로 마성터널 입구 버스전용차로에서 조사한 결과, 버스전용차로를 위반하는 차량의 비율이 99%에 달하는 것으로 조사되었다. 하지만 현재의 경찰관에 의한 인력식 단속은 단속율도 낮고 불필요한 안전문제 및 지체를 발생시킨다. 고속도로 버스전용차로는 6인 이상 탑승한 9인승 이상 승합차도 통행이 가능하기 때문에 승합차량의 승차인원을 검지하는 기술개발이 필요하다. 조도에 관계없는 승차인원 검지를 위해 적외선 카메라를 사용하였고 짧은 차두시간을 감안한 신속한 영상처리 기법으로 YOLOv5 딥러닝 알고리즘을 사용하였다. 개발시스템 성능 검증을 위해 테스트베드 및 실 현장 평가를 실시한 결과, 테스트베드와 실 현장에서 각각 7%, 8% 오차를 나타내 만족할 만한 성능을 보였다. 본 연구 결과물을 현장에 적용할 경우 고속도로 버스전용차로 운영 효율화 및 단속에 따른 불필요한 지체를 감소시킬 수 있을 것으로 기대된다.

Keywords

Acknowledgement

This work was supported by a grant from the Korea Agency for Infrastructure Technology Advancement (KAIA) (Name of Project: Development of HOV-lane enforcement system based on occupancy detection technology). Also, this paper was an improved version of a former paper (High-Occupancy Vehicle Lane Enforcement System, The Open Transportation Journal, Bentham Open, 2021) written by the author in terms of methodology (a new YOLO algorithm) and data (real-world data).

References

  1. Cantelli, M.(2013), Xerox Vehicle Occupancy Detection System, Xerox Innovations Group.
  2. Hao, X., Chen, H., Yang, Y., Yao, C., Yang, H. and Yang, N.(2011), "Occupant Detection through Near-Infrared Imaging", Tamkang Journal of Applied Science and Engineering, vol. 14, no. 3, pp.275-283.
  3. He, K., Zhang, X., Ren, S. and Sun, J.(2016), "Deep Residual Learning for Image Recognition", Proceedings of IEEE Conference of Computer Vision and Pattern Recognition.
  4. Intelligent Transport Systems Performance Evaluation Guidelines, Notification No. 2020-534 of Ministry of Land, Transport and Infrastructure.
  5. Jang, J.(2021), "High-Occupancy Vehicle Lane Enforcement System", The Open Transportation Journal, Bentham Open.
  6. Jung, K.(2012), A Review of the Effectiveness and Feasibility of HOV Lane on Freeway, Korea Transportation Institute.
  7. McCormick Rankin Corporation(2004), Automated Vehicle Occupancy Monitoring Systems for HOV/HOT Facilities, Enterprise Pooled Fund.
  8. Papanikolopoulos, N.(2017), Sensing for HOV/HOT Lanes Enforcement, Final Report 2017-05, University of Minnesota.
  9. Pavlidis, I., Morellas, V. and Papanikolopoulos, N.(2000a), "A Vehicle Occupant Counting System Based on Near-Infrared Phenomenology and Fuzzy Neural Classification", IEEE Transaction of Intelligent Transportation Systems, vol. 1, no. 2, pp.72-85. https://doi.org/10.1109/TITS.2000.880964
  10. Pavlidis, I., Symosek, P., Fritz, B., Bazakos, M. and Papanikolopoulos, N.(2000b), Automatic Detection of Vehicle Occupants: The Imaging Problem and Its Solution, vol. 11, Machine Vision and Applications.
  11. Pavlidis, I., Symosek, P., Fritz, B., Sfarzo, R. and Papanikolopoulos, N. P.(1999a), "Automatic Passenger Counting in the High Occupancy Vehicle (HOV) Lanes", Proceedings of 1999 Annual Meeting of Intelligent Transportation Society of America.
  12. Pavlidis, I., Symosek, P., Fritz, B. and Papanikolopoulos, N. P.(1999b), "Automatic Detection of Vehicle Passengers through Near-Infrared Fusion", Proceedings of 1999 IEEE/IEEJ/JSAI International Conference of Intelligent Transportation Systems.
  13. Redmon, J. and Farhadi, A.(2018), YOLOv3: An Incremental Improvement, arXiv preprint arXiv:1804.02767.
  14. Redmon, J., Divvala, S., Girshick, R. and Farhadi, A.(2015), You Only Look Once: Unified, Real-Time Object Detection, arXiv preprint arXiv:1506.02640.
  15. Roulland, F.(2016), "Xerox Vehicle Passenger Detection System", Proceedings of 23rd World Congress on Intelligent Transport Systems.
  16. Schijns, S. and Eng, P.(2006), High Occupancy Vehicle Lanes-Worldwide Lessons for European Practitioners, vol. 89, WIT Transactions on The Built Environment.
  17. Shorten, C. and Khoshgoftaar, T.(2019), "A Survey on Image Data Augmentation for Deep Learning", Journal of Big Data.
  18. Speciality Construction News, http://www.kscnews.co.kr/news/articleView.html?idxno=9482, 2022.05.01.
  19. Zhu, X., Lyu, S., Wang, X. and Zhao, Q.(2021), "TPH-YOLOv5: Improved YOLOv5 Based on Transformer Prediction Head for Object Detection on Drone-Captured Scenarios", Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops.