Development of Machine Vision System based on PLC

PLC 기반 머신 비전 시스템 개발

  • Received : 2013.09.30
  • Accepted : 2014.02.28
  • Published : 2014.07.01


This paper proposes a machine vision module for PLCs (Programmable Logic Controllers). PLC is the industrial controller most widely used in factory automation system. However most of the machine vision systems are based on PC (Personal Computer). The machine vision system embedded in PLC is required to reduce the cost and improve the convenience of implementation. In this paper, we newly propose a machine vision module based on PLC. The image processing libraries are implemented and integrated with the PLC programming tool. In order to interface the libraries with ladder programming, the ladder instruction set was also designed for each vision library. By use of the developed system, PLC users can implement vision systems easily by ladder programming. The developed system was applied to sample inspection system to verify the performance. The experimental results show that the proposed system can reduce the cost of installing as well as increase the ease-of-implementation.


  1. J. B. An, "PLC development," Journal of Korean Institute of Electrical Engineers (in Korean), vol. 38 no. 12, pp. 37- 40, 1989.
  2. Y. S. Kim and S. Y. Yang, "PDevelopment of the sorting inspection system for screw/bolt using a slant method," Journal of the Korean Society of Machine Tool Engineers (in Korean), vol. 19, no. 5, pp. 698-704, Oct. 2010.
  3. B. J. Park, K. S. Hahn, and E. S. Shin, "Design and implementation of an automated visual inspection system of PDP frames," Journal of Korea Multimedia Society (in Korean), vol. 13, no. 4, pp. 512-525, Apr. 2010.
  4. National Instruments, "PAC for industrial control, the future of control,"
  5. M. Tusch, "High-performance image processing on FPGAs," Xcell Journal, vol. 57, no. 2, pp. 42-44, Apr. 2006.
  6. T. Liu, Z. Ji, Q. Wang, D. Xiao, and S. Zhang, "Research on evaluation of parallelization on an embedded multicore platform," Lecture Note in Computer Science, vol. 5737, pp. 330-340, 2009.
  7. M. Y. Kim, D. J. Seo, Y. M. Kim, and J. K. Ryeu, "Color assortment system of tinted glass pieces using image processing based on hsi color model," Conference on Information and Control Systems (in Korean), pp. 309-310, Oct. 2009.
  8. G. H. Lee and T. H. Park, "Automatic extraction of UV patterns for paper money inspection," Journal of Korean Institute of Intelligent Systems (in Korean), vol. 21, no. 3, pp. 365- 371, 2011.
  9. N. Otsu, "A threshold selection method from gray-level histograms," IEEE Trans. Sys, Man and Cybernetics, vol. 9, no. 1, pp. 62-66, Jan. 1979.
  10. B. R. Lee, Q. Truong, V. Pham, and H. S. Kim, "Automatic thresholding selection for image segmentation based on genetic algorithm," Journal of Institute of Control, Robotics and Systems (in Korean), vol. 17, no. 6, pp. 587- 595, Mar. 2011.
  11. H. Yourui and W. Shuang, "Multi level Thresholding methods for image segmentation with Otsu based on QPSO," Proc. of Congress on Image and Signal Processing, pp. 701-705, May 2008.
  12. M. Luo, Y.-F. Ma, and H.-J. Zhang, "A spatial constrained K-Means approach to image segmentation," in Proceedings of the Joint Conference of International Conference on Information, Communications and Signal Processing, and Pacific Rim Conference on Multimedia, vol. 2, pp. 738-742, Dec. 2003.
  13. T. Eloma and H. Koivistoinen, "On autonomous K-Means clustering," in Proc. of 15th International Symposium on Methodologies for Intelligent Systems, pp. 228-236, May 2005.

Cited by

  1. Defect Classification of Components for SMT Inspection Machines vol.21, pp.10, 2015,
  2. Automatic Classification of SMD Packages using Neural Network vol.21, pp.3, 2015,