DOI QR코드

DOI QR Code

A Study on Cantilever Deformation Inspection Method Using Image Processing

영상처리를 이용한 가동브래킷 변형 검사 기법에 대한 연구

  • Received : 2017.02.28
  • Accepted : 2017.05.15
  • Published : 2017.06.01

Abstract

The risk of facilities in catenary is increasing because the railway section extension and high-speed train service. And visual check of workforce is not enough time to maintain the extensive railway facilities. Accordingly, The technical development trend of maintenance of railway facilities can be seen by automation and application of IT technology, especially the mechanization work and the information technology are spreading in the maintenance work of the train line solved by manpower. In this paper, we describe the method by obtaining the cantilever image using acquisition device and pole inspection system in high speed vehicle, to check the variation of the cantilever component using image processing.

Keywords

References

  1. Lee Byeong Gon, Lee Hyun Chu, Lee See Bin, Park Yong Bum. (2013.6). A Development of Automatic Diagnosis for Electric Inspection Systems of High Speed Railway. Proceedings of Symposium of the Korean Institute of communications and Information Sciences, 105-107.
  2. Woo-Yong Choi, Jong-Gook Park, Byeong-Gon Lee, Yong-Hwan Joo, Seung-Hun Han. (2016.4). Pole Position Detection Method by Using Pole and Character Recognition. The transactions of The Korean Institute of Electrical Engineers, 65(4), 704-710. https://doi.org/10.5370/KIEE.2016.65.4.704
  3. Rong, W., Li, Z., Zhang, W., & Sun, L. (2014, August). An improved CANNY edge detection algorithm. In Mechatronics and Automation (ICMA), 2014 IEEE International Conference on (pp. 577-582). IEEE.
  4. Haykin, S. S., Haykin, S. S., Haykin, S. S., & Haykin, S. S. (2009). Neural networks and learning machines (Vol. 3). Upper Saddle River, NJ, USA: Pearson.
  5. Plaziac, N. (1999). Image interpolation using neural networks. IEEE Transactions on Image Processing, 8(11), 1647-1651. https://doi.org/10.1109/83.799893
  6. Bay, H., Tuytelaars, T., & Van Gool, L. (2006, May). Surf: Speeded up robust features. In European conference on computer vision (pp. 404-417). Springer Berlin Heidelberg.
  7. Pang, Y., Li, W., Yuan, Y., & Pan, J. (2012). Fully affine invariant SURF for image matching. Neurocomputing, 85, 6-10. https://doi.org/10.1016/j.neucom.2011.12.006
  8. Juan, L., & Oubong, G. (2010, July). SURF applied in panorama image stitching. In Image Processing Theory Tools and Applications (IPTA), 2010 2nd International Conference on (pp. 495-499). IEEE.
  9. Strandmark, P., & Gu, I. Y. (2009, June). Joint random sample consensus and multiple motion models for robust video tracking. In Scandinavian Conference on Image Analysis (pp. 450-459). Springer Berlin Heidelberg.
  10. Matas, J., Galambos, C., & Kittler, J. (2000). Robust detection of lines using the progressive probabilistic hough transform. Computer Vision and Image Understanding, 78(1), 119-137. https://doi.org/10.1006/cviu.1999.0831
  11. Kutter, M. (1999, January). Watermarking resistance to translation, rotation, and scaling. In Photonics East (ISAM, VVDC, IEMB) (pp. 423-431). International Society for Optics and Photonics.
  12. Achieved at http://www.eurailscout.com/global/eurailscout/afbeeldingen/factsheets/factsheet_overhead%20line_2016_english.pdf
  13. Achieved at http://www.mermecgroup.com/inspect/catenary-inspection/71/transversal-defects-detection.php