DOI QR코드

DOI QR Code

명암과 움직임 정보를 이용한 포트홀 검출

Pothole Detection using Intensity and Motion Information

  • 김영로 (명지전문대학 컴퓨터정보과) ;
  • 조영태 (한국건설기술연구원 도로교통연구실) ;
  • 류승기 (한국건설기술연구원 도로교통연구실)
  • Kim, Young-Ro (Dept. of Computer Science and Information, Myongji College) ;
  • Jo, Youngtae (Korea Institute of Civil Engineering and Building Technology) ;
  • Ryu, Seungki (Korea Institute of Civil Engineering and Building Technology)
  • 투고 : 2015.09.11
  • 심사 : 2015.10.29
  • 발행 : 2015.11.25

초록

본 논문에서는 명암과 움직임에 따른 다양한 특징점들을 이용하여 포트홀 검출하는 방법을 제안한다. 포트홀 검출 방법은 명암과 움직임에 따라 각각 분할되는 단계와 상호 특징점들의 값에 따라 검출이 결정되는 단계로 이루어진다. 명암을 이용한 분할은 히스토그램을 이용한 이진화 방법을 사용하여 포트홀과 주변 영역을 구분한다. 움직임을 이용한 분할은 먼저 움직임의 변화가 있는 영역을 구분하기 위하여 high pass filtering을 한 후 standard deviation 값을 얻는다. 그리고 도로 촬영 각도, 높이, 속도 등에 따른 움직임 크기를 조정하기 위하여 regression값으로 나눈다. 히스토그램 기반 이진화를 이용하여 이진 영상으로 만든다. 포트홀을 검출하는 결정에서는 후보 영역과 배경 영역과의 특징점들의 비교를 통해서 후보 영역이 포트홀 여부를 판단한다. 실험 결과, 제안하는 방법이 기존 포트홀 검출 방법 보다 향상된 결과를 보이고 포트홀과 유사한 형태들과 구분하는 향상된 결과를 보인다.

In this paper, we propose a pothole detection method using various features of intensity and motion. Segmentation, decision steps of pothole detection are processed according to the values which are derived from feature characteristics. For segmentation using intensity, we use a binarization method using histogram to separate pothole region from background. For segmentation using motion, we filter using high pass filter and get standard deviation value. This value is divided by regression value according to camera environment such as photographing angle, height, velocity, etc. We get binary image by histogram based binarization. For decision, candidate regions are decided whether pothole or not using comparison of candidate and background's features. Experimental results show that our proposed pothole detection method has better results than existing methods and good performance in discrimination between pothole and similar patterns.

키워드

참고문헌

  1. J. Eriksson, L. Girod, B. Hull, R. Newton, S. Madden, H. Balakrishna, "The pothole patrol: using a mobile sensor network for road surface monitoring," Proceeding of the 6th international conference on Mobile systems, applications, and services, pp. 29-39, 2008.
  2. K. T. Chang, J. R. Chang, and J. K. Liu, "Detection of pavement distresses using 3D laser scanning technology," Computing in Civil Engineering, pp. 1-11, 2005.
  3. C. Koch and I. Brilakis, "Pothole detection in asphalt pavement image," Advanced Engineering Informatics, vol. 25, no. 3, pp. 507-515, 2011. https://doi.org/10.1016/j.aei.2011.01.002
  4. Y. Fujita, Y. Mitani, and Y. Hamamoto, "A method for crack detection on a concrete structure," ICPR'06, Hon Kong, pp. 901-904, Aug. 2006.
  5. S. Iyer and S. K. Sinha, "A robust approach for automatic detection and segmentation of cracks in underground pipeline images," Image and Vision Computing, vol. 23, pp. 921-933, 2005. https://doi.org/10.1016/j.imavis.2005.05.017
  6. Salembier, P, "Comparison of some morphological segmentation algorithms based on contrast enhancement. Application to automatic defect detection," Proc. of the EUSIPCO-90 Fifth European Signal Processing Conference, pp. 833-836, 1990.
  7. R. S. Adhikari, O. Moselhi, and A. Bagchi, "Image-based retrieval of concrete crack properties," Journal of the International Society for Geotechnology, vol. 11, no. 2, pp. 315-321, 2012.
  8. Y. -R. Kim, T. Kim, and S. -K. Ryu, "Pothole Detection Method in Asphalt Pavement," Journal of the Institute of Electronics Engineers of Korea, vol. 51, no. 10, pp. 248-255, 2014.
  9. E. M, J. S, and D. K, "Horn-Schunck optical flow with a multi-scale strategy," Image Processing On Line, vol. 3, pp. 151-172, 2013. https://doi.org/10.5201/ipol.2013.20
  10. G. W. Zack, W. E. Rogers, and S. A. Latt, "Automatic measurement of sister chromatid exchange frequency," J. Histochem. Cytochem. vol. 25, no. 7, pp. 741-753, 1977 https://doi.org/10.1177/25.7.70454
  11. N. Otsu, "A threshold selection method from gray-level histograms," IEEE Trans. Systems, Man, and Cybernetics, vol. SMC-9, no. 1, pp. 62-66, Jan. 1979.
  12. T. Leung and J. Malik, "Representing and recognizing the visual appearance of materials using three-dimensional textons," Int. J. Comput. Vision, vol. 43, pp. 29-44, 2001 https://doi.org/10.1023/A:1011126920638
  13. C. Schmid, "Constructing models for content-based image retrieval," in Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 39-45, 2001
  14. J. -M. Sung, H. -G. Ha, and B. -Y. Choi, "Image thresholding based on within-class standard deviation," Journal of the Institute of Electronics Engineers of Korea, vol. 50, no. 7, pp. 1844-1852, 2013.

피인용 문헌

  1. A Study on the Usage the Pavement Smoothness Detection Equipment for Management of Deteriorated Roadways vol.17, pp.4, 2015, https://doi.org/10.9798/kosham.2017.17.4.41