Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
권호 표지
DOI QR코드

DOI QR Code

Illumination-Robust Load Lane Color Recognition based on S-color Space

조명변화에 강인한 S-색상공간 기반의 차선색상 판별 방법

  • Baek, Seung-Hae (Orbotech Korea) ;
  • Jin, Yan (Hyundai Motor Technology & Engineering Center) ;
  • Lee, Geun-Mo (School of Computer Science and Engineering, Kyungpook National University) ;
  • Park, Soon-Yong (School of Computer Science and Engineering, Kyungpook National University)
  • Received : 2018.01.05
  • Accepted : 2018.01.29
  • Published : 2018.03.28
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, we propose a road lane color recognition method from the image obtained from a driving vehicle. In autonomous vehicle techniques, lane information becomes more important as the level of autonomous driving such as lane departure warning and dynamic lane keeping assistance is increased. In particular the lane color recognition, especially the white and the yellow lanes, is necessary technique because it is directly related to traffic accidents. In this paper, color information of lane and road area is mapped to a 2-dimensional S-color space based on lane detection. And the center of the feature distribution is obtained by using an improved mean-shift algorithm in the S-color space. The lane color is determined by using the distance between the center coordinates of the color features of the left and right lanes and the road area. In various illumination conditions, about 97% color recognition rate is achieved.

본 논문에서는 주행하는 차량에 탑재된 카메라에서 획득한 도로 영상에서 차선의 색상을 판별하는 방법을 제안하였다. 자동차의 자율주행기술에 있어 차선 정보는 차선이탈방지(ldws), 능동적 차선유지(lkas), 고속도로주행보조(hda) 등의 자율주행의 레벨(level)이 올라갈수록 중요하다. 특히 차선의 색상, 특히 흰색 및 황색 차선의 구별은 교통사고와 직접적인 관련이 있는 정보이기에 더욱 필요한 기술이다. 본 논문에서는 주행 차선 검출 결과를 기반으로 차선 및 도로의 관심 영역을 추출하고 각 영역의 컬러 정보를 2차원 S-색상 공간으로 투영하였다. S-공간에 투영된 색상의 특징 분포에서 개선된 mean-shift 알고리즘을 이용하여 특징의 무게중심을 구하였다. 좌, 우 차선과 도로영역의 색상특징의 중심점들 사이의 거리 정보를 이용하여 차선의 색상을 판별하였다. 다양한 조명환경에서 약 97%의 색상 인식 성공률을 보였다.

Keywords

References

  1. J. Y. Lee, S. W. Moon, K.S. Yi, B.M. Yun., and S.B., Yu, "A lane departure warning algorithm and forward collision warning algorithm with an only one camera." The Korean Society of Automotive Engineers Annual Conference Proceedings, pp. 1853-1860, 2009.
  2. M. Y. Yoon, J. K. Choi, J. E. Jung, K. S. Boo, and H. S. Kim, "Development of vehicle side collision avoidance system with virtual driving environments." Journal of the Korean Society for Precision Engineering, vol. 30, no. 2, pp. 164-170, Feb, 2013. https://doi.org/10.7736/KSPE.2013.30.2.164
  3. A. Mammeri, G. Lu, and A. Boukerche. "Design of lane keeping assist system for autonomous vehicles." IEEE 7th International Conference on New Technologies, Mobility and Security (NTMS), pp. 1-5, 2015.
  4. M. Tsogas, A. Polychronopoulos, and A. Amditis, "Using digital maps to enhance lane keeping support systems," IEEE Intelligent Vehicles Symposium, pp. 148-153, 2007.
  5. C. S. Bae, J. H. Lee, and S. B. Cho. "Lane detection algorithm using morphology and color information." Journal of the Institute of Electronics Engineers of Korea SD vol. 48, no. 6, pp. 15-24, June, 2016.
  6. K. H. Jang, and S. W. Kwak. "Fast center lane detection method for vehicle applications." Journal of the Korea Institute of Electronic Communication Sciences vol. 9, no. 6, pp. 649-656, June, 2014. https://doi.org/10.13067/JKIECS.2014.9.6.649
  7. H. C. Choi, and S. Y. Oh, "Illumination invariant lane color recognition by using road color reference & neural networks." The 2010 International Joint Conference on Neural Networks (IJCNN), pp. 1-5, 2010.
  8. H. J. Jang, S. H. Baek, and S. Y. Park, "Lane marking detection in various lighting conditions using robust feature extraction," In Proceedings of the 22th international Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, pp. 83-88, 2014.
  9. Z. F. Gao, W. Bo, M. J. Dong, and Y. S. Shi, "Intelligent real-time lane detection for vehicles in urban streets," International Conference on Information Science and Control Engineering, pp. 1-20, 2012.
  10. B. Paulchamy, K. Archana, A. Bruno, I. Divya, and M. Krishnan, "A novel approach towards road safety based on investigational survey using image processing and user interface systems," International Education and Research Journal, vol. 3, no. 3, pp. 105-108, Mar, 2017.
  11. K. Y. Chiu, and S. F. Lin, "Lane detection using color-based segmentation." Proceedings of IEEE Intelligent Vehicles Symposium, pp. 706-711, 2005.
  12. Z. Ying, and L. Ge, "Robust lane marking detection using boundary-based inverse perspective mapping." IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1921-1925, 2016.
  13. K. Du, Y. F. Ju, "Mean-Shift tracking algorithm with adaptive block color histogram," IEEE 2nd International Conference on. Consumer Electronics, Communications and Networks (CECNet), pp. 2692-2695, 2012.
  14. S. Park, S. Baek, and C. Choi, "Simulation of Radiation Imaging based on the Scanning of Pin-hole Stereo Vision Sensors," Journal of the Korea Institute of Information and Communication Engineering, vol. 18, no. 7, pp. 1671-1680, July, 2014 https://doi.org/10.6109/jkiice.2014.18.7.1671