Automated Inspection System for Brake Shoe of Rolling Stock

철도차량용 제륜자의 자동 검사 시스템

  • Kim, Hyun-Cheol (Dept. of Electronics Computer Engineering, Hanyang University) ;
  • Kim, Whoi-Yul (Dept. of Electronics Computer Engineering, Hanyang University)
  • 김현철 (한양대학교 전자컴퓨터통신공학과) ;
  • 김회율 (한양대학교 전자컴퓨터통신공학과)
  • Published : 2009.11.25

Abstract

In this paper, we have proposed an automated system that accurately measures the thickness and unbalanced wear of brake shoes, and the distance between brake shoes and wheels for travelling rolling stock. The images of brake shoes are captured automatically while rolling stock is passing by an inspection station. And in order to measure the thickness, etc. the locations of brake shoes are first determined because the locations are not the same in the captured image. Toward this goal, shadow regions between the brake shoes and wheels are utilized that are common in all captured images. The boundary of the shadow regions is modeled by an second order polynomial, and constrained curve fitting method is adopted to detect a curve (the initial curve) that passes through the regions. Then, three curves that correspond to the front, back of brake shoes and wheels, and a line that passes through the vertical surface of brake shoes are detected using the initial curve and intensity change information. Finally, the thickness, etc. are calculated using the detected curves and line, and experimental results showed that the brake shoe thickness was measured with an accuracy of 0.654mm.

본 논문에서는 이동 중인 철도차량 제륜자의 두께, 편마모 및 제륜자와 차륜 사이의 거리를 자동으로 측정하는 시스템을 제안한다. 철도차량의 제륜자는 촬영 시스템이 설치된 곳을 지날 때 자동으로 촬영되며, 두께 등을 측정하기 위해 가장 먼저 영상 내에서 제륜자의 위치를 검출한다. 이는 제륜자의 위치가 영상 마다 다르기 때문이며 이를 적응적으로 찾기 위해, 제륜자와 차륜 사이에 나타나는 그림자 영역을 이용한다. 그림자 영역의 경계는 9차 다항식을 통해 모델링되며, 그 영역을 지나는 임의의 곡선 (초기 곡선)은 제약 곡선 피팅 방법을 통해 검출된다. 다음은 검출된 초기 곡선과 명암도 변화 정보를 이용하여 제륜자의 앞과 뒤, 차륜 상의 세 곡선 및 제륜자의 수직면을 지나는 직선을 검출한다. 최종적으로, 앞서 구한 곡선과 직선을 이용하여 제륜자의 두께, 편마모 및 제륜자와 차륜 사이의 거리를 측정한다. 실험에서는 제안된 방법을 실제 선로에서 촬영한 영상에 대해 적용해 보았으며, 그 결과 평균 0.654mm의 두께 측정 오차를 나타내었다.

Keywords

References

  1. H. Sato, H. Nishii, and S. Adachi, "Automatic Thickness Measuring System by Image Processing for Brake Shoes of Traveling Rolling Stock," Kawasaki Steel Technical Report, no. 27, 1992
  2. http://www.intra-corp.net
  3. http://www.wabco-auto.com
  4. G. Glogowski and J. Sawatzky, "Computer Vision Measurement of Brake Shoes for Fort Garry Industries," B.Sc. Thesis in University of Manitoba, 2006
  5. R. C. Gonzalez and R. E. Woods, Digital Image Processing, Prentice Hall, 2002
  6. R. O. Duda and P. E. Hart, "Use of the Hough Transformation to Detect Lines and Curves in Pictures," Communications of the ACM, vol. 15, pp. 11-15, 1972 https://doi.org/10.1145/361237.361242
  7. G. Strang, Linear Algebra and Its Applications, Brooks/Cole
  8. A. Fitzgibbon, M. Pilu, and R. B. Fisher, "Direct Least Square Fitting of Ellipses," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 21, no. 5, pp. 476-480, 1999 https://doi.org/10.1109/34.765658
  9. W. Gander, G. H. Golub, and R. Strebel, "Least-Squares Fitting of Circles and Ellipses," BIT, no. 34, pp. 558-578, 1994 https://doi.org/10.1007/BF01934268
  10. G. Taubin, "Estimation of Planar Curves, Surfaces and Non-Planar Space Curves Defined by Implicit Equations, With Applications to Edge and Range Image Segmentation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 13, no. 11, pp. 1115-1138, 1991 https://doi.org/10.1109/34.103273
  11. T. F. Coleman and Y. Li, "An Interior Trust Region Approach for Nonlinear Minimization Subject to Bounds," SIAM Journal on Optimization, vol. 6, pp. 418-445, 1996
  12. T. F. Coleman and Y. Li, "On the Convergence of Reflective Newton Methods for Large-Scale Nonlinear Minimization Subject to Bounds," Mathematical Programming, vol. 67, no. 2, pp. 189-224, 1994 https://doi.org/10.1007/BF01582221
  13. J. E. Dennis, Jr., Nonlinear least squares and equations, in. The State of the Art of Numerical Analysis edited by D. Jacobs, Academic Press, pp. 269-312, 1977
  14. D. W. Marquardt, "An Algorithm for Least-Squares Estimation of Nonlinear Parameters," SIAM Journal on Applied Mathematics, vol. 11, pp. 431-441, 1963 https://doi.org/10.1137/0111030
  15. J. F. Canny, "A Computational Approach To Edge Detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 8, pp. 679-714, 1986 https://doi.org/10.1109/TPAMI.1986.4767851
  16. http://www.vision.caltech.edu/bouguetj/calib_doc/