원형구멍 정밀 내경측정을 위한 영상처리 개선에 관한 연구

A Study on Improvement of Image Processing for Precision Inner Diameter Measurement of Circular Hole

  • 박창용 (국립금오공과대학교 기계시스템공학과) ;
  • 권현규 (국립금오공과대학교 기계시스템공학과) ;
  • 이정화 (국립금오공과대학교 기계시스템공학과) ;
  • 장화신 (국립금오공과대학교 기계시스템공학과)
  • Park, ChangYong (Mechanical System Engineering, Kumoh National Institute of Technology) ;
  • Kweon, HyunKyu (Mechanical System Engineering, Kumoh National Institute of Technology) ;
  • Li, JingHua (Mechanical System Engineering, Kumoh National Institute of Technology) ;
  • Zhang, Hua Xin (Mechanical System Engineering, Kumoh National Institute of Technology)
  • 투고 : 2017.07.26
  • 심사 : 2017.09.18
  • 발행 : 2017.09.30

초록

In this paper, the measurement of the inner diameter dimension of the circular hole by using a machine vision system was studied. This paper was focused on the theory and key technologies of machine vision inspection technology for the improvement of measurement accuracy and speed of the micro circular holes. A new method was proposed and was verified through the experiments on Gray conversion, binarization, edge extraction and Hough transform in machine vision system processes. Firstly, the Hough transform was proposed in order to improve the speed increase and implementation ease, it demonstrated the superiority of Hough transform and improvement through a comparative experiment. Secondly, we propose a calibration method of the system in order to obtain exactly the inner diameter of the circular hole. Finally, we demonstrate the reliability of the entire system as a MATLAB-based implementation of the GUI program, measuring the inner diameter of the circular hole through the circular holes of different dimensions measuring experiment.

키워드

참고문헌

  1. Wang, Z.H. and He, W.J., "Further Research on Image Processing of Micro-dimension Measurement," Chinese Journal of Scientific Instrument, pp. 235-236, (2001).
  2. Han, J.H., and Zhao, S.S., "Research on subpixel detecting on-line system based on machine vision for inner diameter of bearings," Proceedings of the 2007 IEEE International Conference on Robotics and Biomimetics, vol. 12(3), pp. 15-18, (2007).
  3. T LI, and N Ning, "Study on circle locating technology based on machine vision," Computer Engineering and Application, vol. 48(9), pp. 153-156, (2012). https://doi.org/10.3778/j.issn.1002-8331.2012.09.044
  4. Xu, G.B., Zhou, M.J., and Xiong, Z.G., "An improved adaptive fusion edge detection algorithm for road images," Advances in Information Sciences and Service Sciences, vol. 4(4), pp. 129-137, (2012).
  5. Sun, W., Zhang, X.R., and Tang, H.Q., "Lane Coordination Detection Based on Hough Transformation and Least Square Fitting," Opto-Electronic Engineering, vol. 38(10), pp. 13-19, 2011. https://doi.org/10.3969/j.issn.1003-501X.2011.10.003
  6. Xu, L., Oja, E., and Kultanen, P., "A new curve detection method: randomized Hough transform(rth)," Pattern Recognition Letters, vol. 11. pp. 331-338, 1990. https://doi.org/10.1016/0167-8655(90)90042-Z
  7. Ballard, D.H., "Generalizing the Hough transform to detect arbitrary shape," Pattern Recognition, vol. 13, No.2, pp.111-122, (1981). https://doi.org/10.1016/0031-3203(81)90009-1
  8. Zhao, G.X. and Huang, S., "An Improved Randomized Hough Method of Circle Detection," Computer Technology and Developmrnt, 18(4): pp. 77-79, (2008).
  9. Zhu, X.L., Gao, C.H., and He, B.W., "An Improved Method of Hough Transform Circle Detection Based on the Midpoint Circle-Producing Algorithm," Journal of Engineering Graphics, pp. 29-33, (2010).
  10. Weiscope Optical Technology, Calibration slide, www.weiscope.com