Journal of IKEEE (전기전자학회논문지)

Institute of Korean Electrical and Electronics Engineers (한국전기전자학회)
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Efficient Real-time Lane Detection Algorithm Using V-ROI

V-ROI를 이용한 고효율 실시간 차선 인식 알고리즘

  • Dajun, Ding (Dept. of Electronic Engineering, Soongsil University) ;
  • Lee, Chanho (School of Electronic Engineering, Soongsil University)
  • Received : 2012.10.03
  • Accepted : 2012.11.19
  • Published : 2012.12.31
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Abstract

Information technology improves convenience, safety, and performance of automobiles. Recently, a lot of algorithms are studied to provide safety and environment information for driving, and lane detection algorithm is one of them. In this paper, we propose a lane detection algorithm that reduces the amount of calculation by reducing region of interest (ROI) after preprocessing. The proposed algorithm reduces the area of ROI a lot by determining the candidate regions near lane boundaries as V-ROI so that the amount of calculation is reduced. In addition, the amount of calculation can be maintained almost the same regardless of the resolutions of the input images by compressing the images since the lane detection algorithm does not require high resolution. The proposed algorithm is implemented using C++ and OpenCV library and is verified to work at 30 fps for realtime operation.

자동차가 IT 기술과 융합되면서 편의성과 안전성 그리고 성능이 좋아지고 있다. 이와 관련하여 최근 자동차의 주행시 안전 및 주변 환경과 관련된 정보를 제공하기 위한 많은 알고리즘이 연구되고 있으며 차선 인식 또한 그 중 하나이다. 본 논문에서는 입력된 영상에서 차선 경계선을 인식한 뒤 ROI를 경계선 주변으로 제한하여 연산량을 줄이는 알고리즘을 제안한다. 제안된 알고리즘에서는 선처리 과정을 통해 차선 경계선으로 추정되는 영역의 주변만을 ROI로 지정하는 V-ROI를 이용하여 연산 영역을 줄이고 이를 통해 연산량과 연산 시간을 줄인다. 또한 차선 인식의 경우 고해상도의 영상이 필요하지 않으므로 입력 영상을 축소하여 차선 인식 알고리즘을 적용하는 방법을 통하여 영상의 해상도에 관계없이 연산량을 비슷하게 유지할 수 있다. 제안한 알고리즘을 C++와 OpenCV 라이브러리를 이용하여 구현하였으며 초당 30 프레임 이상을 처리하는 실시간 동작을 확인하였다.

Keywords

References

  1. Qing Lin, Youngjoon Han and Hernsoo Hahn, "Real-time Lane Detection Based on Extended Edge-linking Algorithm", Second International Conference on Computer Research and Development, pp. 725-730, May 7-10, 2010, Kuala Rumpur, Malaysia
  2. Joel C. McCall and Mohan M. Trivedi, "Video-based Lane Estimation and Tracking for Driver Assistance: Survey, System, and Evaluation", IEEE Transactions on Intelligent Transportation Systems, Vol. 7, pp. 20-37, 2006 https://doi.org/10.1109/TITS.2006.869595
  3. Xu Zhe, Li Zhifeng, "A robust lane detection method in the different scenarios", Proceedings of 2012 IEEE International Conference on Mechatronics and Automation, pp. 1358-1363, Aug. 5-8, 2012, Chengdu, China
  4. Shengyan Zhou, Yanhua Jiang, Junqiang Xi, "A Novel Lane Detection based on Geometrical Model and Gabor Filter ", 2010 IEEE Intelligent Vehicles Symposium, pp. 59-64, June 21-24, 2010, San Diego, USA
  5. Claudio Rosito Jung, Christian Roberto Kelber, "Lane following and lane departure using a linear-parabolic model", Image and Vision Computing, Vol. 5, pp. 1192-1202, 2005
  6. Vijay Gaikwad, Shashikant Lokhande, "An improved lane departure method for Advanced Driver Assistance System", International Conference on Computing, Communication and Applications (ICCCA), pp. 1-5, Feb. 22-24, 2012, Tamilnadu, India
  7. P.M. Daigavan and P. R.Bajaj, "Road Lane Detection with Improved Canny Edges Using Ant Colony Optimization", Third International Conference on Emerging Trends in Engineering and Technology, pp. 76-80, Nov. 19-21, 2010, Goa, India
  8. Lee Kim Kuan, Ismail N.H, "Lane Guidance Warning System", International Conference on Computer and Communication Engineering (ICCCE 2012), pp. 864 - 868, July 3-5, 2012, Kuala Lumpur, Malaysia
  9. Jung Gap Kuk, Jae Hyun An, "Fast lane detection & tracking based on Hough transform with reduced memory requirement", 13th International IEEE Annual Conference on Intelligent Transportation Systems, pp. 1344-1349, Sep. 19-22, 2010, Madeira Island, Portugal
  10. Nobuyuki Otsu, "A threshold selection method from gray-level histograms". IEEE Trans. Sys., Man., Cyber. Vol. 9(1), pp. 62-66. 1979 https://doi.org/10.1109/TSMC.1979.4310076