DOI QR코드

DOI QR Code

Development of Bolt Tap Shape Inspection System Using Computer Vision Technology

컴퓨터 비전 기술을 이용한 볼트 탭 형상 검사 시스템 개발

  • 박양재 (가천대학교 IT대학 컴퓨터공학과)
  • Received : 2018.01.26
  • Accepted : 2018.03.20
  • Published : 2018.03.28

Abstract

Computer vision technology is a component inspection to obtain a video image from the camera to the machine to perform the capabilities of the human eye with a field of artificial intelligence, and then analyzed by the algorithm to determine to determine the good and bad of production parts It is widely applied. Shape inspection method was used as how to identify the location of the start point and the end point of the search range, measure the height to the line scan method, in such a manner as to determine the presence or absence of the bolt tabs average brightness of the inspection area in a circular scan type value And the degree of similarity was calculated. The total time it takes to test in the test performance tests of two types of bolts tab enables test 300 min, and demonstrated the accuracy and efficiency of the inspection on the production line represented a complete inspection accuracy.

Keywords

Computer;Machine Vision;Shape Inspection;Pattern Recognition;Bolt Inspection;Nut Inspection

References

  1. S. B. Baek, K. Y. Lee, W. J. Joo, K. Park & S. W. Ra. (2011). Improvement of the optical characteristics of vision system for precision screws using ray tracing simulation. Trans. KSPE, 28, 1194-1102.
  2. H. J. Yang, D. H. Kim. & Y. G. Seo. (2017). Noise-robust Hand Region Segmentation In RGB Color-baseed Real-time Image. Journal of Digital Contents Society, 18(8), 1603-1613. https://doi.org/10.9728/DCS.2017.18.8.1603
  3. T. R Singh, S. Roy, O. Imocha, T. Sinam & M. Singh. (2011). A new local adaptive thresholding technique in binarization, IJCSI International Journal of computer science issues, 8(6), 271-277.
  4. S. K. Hwang. (2015). Visual C++ Image Processing Pgrogramming. Seoul. Gilbut Publishing.
  5. Wikipedia, Canny Edge Detector, (2018), https://en.wikipedia.org/wiki/Canny_edge_detector,
  6. H. S. Kim.(2017), Gpu based real time lane detection using compact hough transform. Ph.D. dissertation, Kyungbok University.
  7. OpenCV. (2017), Opencv documentation, https://docs.opencv.org/2.4.13.3/
  8. S. J. Kim & S.C. Lee. (2014). Development of Inspection System for Surface of a Shack Absorber Rod using Machine Vision. Journal of the Korea Academia-Industrial Cooperation Society, 15(6), 3416-3422. https://doi.org/10.5762/KAIS.2014.15.6.3416
  9. K. H. Kwak, D. F. Huber, H. Badino & T. Kanade.(2011). Extrinsic calibration of a single line scanning lidar and a camera. intelligent robots and systems (IROS), IEEE/RSJ International conference on. 3283-3285. San Francisco, CA, USA.
  10. Gao, H. & Ye, X. & Li, J.(2013). A simple line sensing method by laser line scanning for line scale measurement, Proceedings -SPIE the international socity for optical engineering, 875. Beijing, China.
  11. Y. C. Kim, Y. M, Kim, S. G. Kim, H. B. Kim & M.T. Cho. (2016). Development of the Mechenical System and Vision Algorithm for the External Appearance Test Using Vision Image Processing. Journal of the Korea Academia-Industrial Cooperation Society, 17(2), 202-208. https://doi.org/10.5762/KAIS.2016.17.2.202
  12. J. S. Yun. & .H. Kim. (2017), An Improvement of Histogram Equalization Using Edge Information of an Image. Journal of Korea Multimedia Society, 20(2), 188-195. https://doi.org/10.9717/kmms.2017.20.2.188
  13. K. H. Kwak, D. F. Huber, H. Badino & T. Kanade. (2011). Extrinsic calibration of a single line scanning lidar and a camera. intelligent robots and systems (IROS), IEEE/RSJ International conference on. 3283-3285. San Francisco, CA, USA.
  14. S. J. Kim & S. C. Lee. (2014). Development of Inspection System for Surface of a Shock Absorber Rod using Machine vision. Journal of the Korea Academia-industrial cooperation Society, 15(6), 3416-3422. https://doi.org/10.5762/KAIS.2014.15.6.3416