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Recognition Direction Improvement of Target Object for Machine Vision based Automatic Inspection

머신비전 자동검사를 위한 대상객체의 인식방향성 개선

  • Hong, Seung-Beom (Graduate School, Inje University) ;
  • Hong, Seung-Woo (Graduate School of Industry Convergence, Inje University) ;
  • Lee, Kyou-Ho (Dept. of Information and Communications Engineering, Inje University)
  • Received : 2019.09.02
  • Accepted : 2019.09.17
  • Published : 2019.11.30

Abstract

This paper proposes a technological solution for improving the recognition direction of target objects for automatic vision inspection by machine vision. This paper proposes a technological solution for improving the recognition direction of target objects for automatic vision inspection by machine vision. This enables the automatic machine vision inspection to detect the image of the inspection object regardless of the position and orientation of the object, eliminating the need for a separate inspection jig and improving the automation level of the inspection process. This study develops the technology and method that can be applied to the wire harness manufacturing process as the inspection object and present the result of real system. The results of the system implementation was evaluated by the accredited institution. This includes successful measurement in the accuracy, detection recognition, reproducibility and positioning success rate, and achievement the goal in ten kinds of color discrimination ability, inspection time within one second and four automatic mode setting, etc.

본 논문은 머신비전기반 자동검사를 위한 대상객체의 인식방향성 개선 연구로서, 영상카메라에 의한 자동 비전검사의 과정에서 제한성이 따르는 대상 객체의 인식방향성을 개선하는 방법을 제안한다. 이를 통하여 머신비전 자동검사에서 시험대상물의 위치와 방향에 상관없이 검사대상의 영상을 검출할 수 있게 함으로써 별도 검사지그의 필요성을 배제하고 검사과정의 자동화 레벨을 향상시킨다. 본 연구에서는 검사대상으로서 와이어 하네스 제조과정에서 실제 적용할 수 있는 기술과 방법을 개발하여 실제 시스템으로 구현한 결과를 제시한다. 시스템구현 결과는 공인기관의 평가를 통하여, 정밀도, 검출인식도, 재현률 및 위치조정 성공률에서 모두 성공적인 측정결과를 얻었고, 당초 설정하였던 10종류의 컬러구별 능력, 1초 이내 검사시간, 4개 자동모드 설정 등에서도 목표달성을 확인하였다.

Keywords

References

  1. C. Steger, M. Ulrich, and C. Wiedemann, Machine Vision Algorithms and Applications, Wiley-VCH, Weinheim, 2008.
  2. J. Beyerer, F. P. Len, and C. Frese, Machine Vision-Automated Visual Inspection: Theory, Practice and Applications, Springer, Berlin, 2016.
  3. A. Owen-Hill, "Robot Vision vs Computer Vision: What's the Difference?," Robotics Tomorrow, Jul. 2016.
  4. Computer Vision vs Robot Vision Understanding The Difference [Internet]. Available: http://www.oodlestechnologies.com/blogs/Computer-Vision-vs-Robot-Vision-Understanding-The-Difference.
  5. Cognex, Introduction to machine vision, Assembly Magazine, 2016.
  6. I. A. Kamal, and M. A. Al-Alaoui, "Online machine vision inspection system for detecting coating defects in metal lids," in Proceedings of the International Multiconference of Engineers and Computer Scientists 2008 Vol II (IMECS 2008), Hong Kong, 2008.
  7. E. Malamas, E. Petrakis, M. Zervakis, L. Petit, and J.-D. Legat, "A survey on industrial vision systems applications and tools," Image Vision Computing, vol. 21, no. 2, pp. 171-188, 2003. https://doi.org/10.1016/S0262-8856(02)00152-X
  8. M. Shirvaikar, "Trends in automated visual inspection," Journal of RealTime Image Processing, vol. 1, no. 1, pp. 41-43, 2006. https://doi.org/10.1007/s11554-006-0009-6
  9. E. Guerra, and J. Villalobos, "A three-dimensional automated visual inspection system for SMT assembly," Computers and Industrial Engineering, vol. 40, no. 1, pp. 175-190, 2001. https://doi.org/10.1016/S0360-8352(01)00016-X
  10. Y. R. Charles, and R. Ramraj, "A novel local mesh color texture pattern for image retrieval system," AEU - International Journal of Electronics and Communications, vol. 70, pp. 225-233, 2016. https://doi.org/10.1016/j.aeue.2015.11.009
  11. W. Lee, and K. Cao, "Application of Machine Vision to Inspect a Wiring Harness," in Proceeding of 2019 IEEE International Conference on Industrial Cyber Physical Systems (ICPS), Taipei, Taiwan, pp. 6-9, May. 2019.
  12. G. M. Shi, and W. Jian, "Wiring harness assembly detection system based on image processing technology," in Proceeding of 2011 International Conference on Electronics Communications and Control (ICECC), pp. 2397-2400, 2011.
  13. S. Ghidoni, M. Finotto, and E. Menegatti, "Automatic Color Inspection for Colored Wires in Electric Cables," IEEE Transactions on Automation Science and Engineering, vol. 12, pp. 596-607, 2015. https://doi.org/10.1109/TASE.2014.2360233