Machine Vision Systems for Industrial Quality Control Inspections

  • Ricardo Luhm Silva
  • Marcelo Rudek
  • Anderson Luis SzejkaEmail author
  • Osiris Canciglieri Junior
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 540)


In this paper we introduce Machine Vision System (MVS) for industrial quality control inspections presenting new perspectives with the recent developments of Artificial Intelligence (AI). A brief literature review is provided which indicates a substantial growth of machine vision new studies and an improved workflow is proposed to include these findings. Besides already existing machine vision solutions there is space to increase detection in quality control inspection and reduce current implementation constraints and technical limitations. The paper shows MVS new development and evinces that a deeper understanding of AI, MVS limitations is needed to provide a clearer path for future studies.


Machine vision Industrial inspection Machine learning Artificial intelligence 



This work was supported by Araucaria Foundation for Science and Technology/ FA-PR under Grant 40/2017 and Renault Brazil.


  1. 1.
    Vergara-Villegas, O.O., Cruz-Sánchez, V.G., de Jesús Ochoa-Domínguez, H., de Jesús Nandayapa-Alfaro, M., Flores-Abad, Á.: Automatic product quality inspection using computer vision systems. In: García-Alcaraz, J.L., Maldonado-Macías, A.A., Cortes-Robles, G. (eds.) Lean Manufacturing in the Developing World, pp. 135–156. Springer, Cham (2014). Scholar
  2. 2.
    Satorres, S., Gómez, J., Gámez, J., Sánchez, A.: A machine vision system for defect characterization on transparent parts with non-plane surfaces. Mach. Vis. Appl. 23(1), 1–13 (2012). Scholar
  3. 3.
    Szkilnyk, G.: Vision-based fault detection in assembly automation, Thesis, Queen’s University, Canada (2012)Google Scholar
  4. 4.
    Golnabi, H., Asadpour, A.: Design and application of industrial machine vision systems. Robot. Comput.-Integr. Manuf. 23(6), 630–637 (2007). Scholar
  5. 5.
    Labudzki, R., Legutko, S., Raos, P.: The essence and applications of machine vision. Tehnicki Vjesnik 21(4), 903–909 (2014)Google Scholar
  6. 6.
    Lerones, P., Fernández, J., García-Bermejo, J., Zalama, E.: Total quality control for automotive raw foundry brake disks. Int. J. Adv. Manuf. Technol. 27(3–4), 359–371 (2005). Scholar
  7. 7.
    Pérez, L., Rodríguez, Í., Rodríguez, N., Usamentiaga, R., García, D.F.: Robot guidance using machine vision techniques in industrial environments: a comparative review. Sensors 16(3), 335 (2016). Scholar
  8. 8.
    Wang, J., Ma, Y., Zhang, L., Gao, R. X., Wu, D.: Deep learning for smart manufacturing: methods and applications. J. Manuf. Syst. (2018). In-press
  9. 9.
    Chauhan, V., Surgenor, B.: A comparative study of machine vision based methods for fault detection in an automated assembly machine. Procedia Manuf. 1, 416–428 (2015). Scholar
  10. 10.
    Su, X., Zhang, Q.: Dynamic 3-D shape measurement method: a review. Opt. Lasers Eng. 48(2), 191–204 (2010). Scholar
  11. 11.
    Van der Jeught, S., Dirckx, J.J.: Real-time structured light profilometry: a review. Opt. Lasers Eng. 87, 18–31 (2016). Scholar
  12. 12.
    Zhong, R.Y., Xu, X., Klotz, E., Newman, S.T.: Intelligent manufacturing in the context of Industry 4.0: a review. Engineering 3(5), 616–630 (2017). Scholar
  13. 13.
    Pal, A., Dasgupta, R., Saha, A., Nandi, B.: Human-like sensing for robotic remote inspection and analytics. Wirel. Pers. Commun. 88(1), 23–38 (2016). Scholar
  14. 14.
    Leitão, P., Colombo, A.W., Karnouskos, S.: Industrial automation based on cyber-physical systems technologies: prototype implementations and challenges. Comput. Ind. 81, 11–25 (2016). Scholar
  15. 15.
    Nagato, T., Koezuka, T.: Automatic generation of image-processing programs for production lines. Fujitsu Sci. Tech. J. 52(1), 27–33 (2016)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2018

Authors and Affiliations

  • Ricardo Luhm Silva
    • 1
  • Marcelo Rudek
    • 1
  • Anderson Luis Szejka
    • 1
    Email author
  • Osiris Canciglieri Junior
    • 1
  1. 1.Industrial and Systems Engineering Graduate ProgramPontifical Catholic University of ParanaCuritibaBrazil

Personalised recommendations