Machine Vision Solutions in Automotive Industry

  • Pinnamaneni Bhanu PrasadEmail author
  • N. Radhakrishnan
  • S. Shankar Bharathi
Part of the Studies in Computational Intelligence book series (SCI, volume 543)


Present day consumers have wide variety of demands and needs leads to increased complexity in automobiles. The price war and high quality, imposes the automobile manufacturers to have flexible design with zero defects in a highly competitive market. Unlike other industry, the quality of automobile depends on parts (manufactured and supplied by third party supplier) used and on the assembling the vehicle. To achieve the high quality that is demanded by the customers, manufacturers and their suppliers must rely on Machine Vision to prevent defects at multiple stages of production. Machine Vision can be used to inspect the quality of automobile parts, pick and place using robots, assembly line (inspection before, after and placement verification), to find missing parts, completeness, welding and painting guiding on finished automotive bodies. In addition to this, Machine Vision is also used for parts traceability decoded by reading OCR, data matrix and barcode. Different automobiles can have different quality of parts depending on price range. Machine Vision can also be used to classify automotive parts based on the required quality using measurements. This publication explains the basics of machine vision and explore the solutions that can be used in automobile industry at different stages of production.


Image source X-ray images Mathematical morphology Image processing Machine vision Inspection Quality control Automobile parts Assembly line Pick and place Robot Compound annual growth rate (CAGR) 


  1. 1.
  2. 2.
    P. Bhanu Prasad, Machine vision systems and image processing with applications. J. Innov. Comput. Sci. Eng. 3(1) (2013)Google Scholar
  3. 3.
    G. Hollows, S. Singer, in Matching Lenses and Sensors, Sision System Design, 2009Google Scholar
  4. 4.
    P. Bhanu Prasad, M. Coster, J.L. Chermant, C. Lantuejoul, Basic mathematical morphological tools for analysis of 3-D structures. Acta Stereologica 6, 773 (1987)Google Scholar
  5. 5.
    P. Sujatha, P. Bhanu Prasad, J.H. Johnson, J.L. Chermant, Low level vision system for 3-dimensional image analysis. Acta Stereologica 8/2, 569–574 (1989)Google Scholar
  6. 6.
    R.C. Gonzalez, in Digital Image Processing, Pearson Education India, 2009Google Scholar
  7. 7.
    R.C. Gonzalez, R.E. Woods, S.L. Eddins, in Digital Image Processing Using Matlab, Pearson Education India, 2004Google Scholar
  8. 8.
    J. Serra, Image Analysis and Mathematical Morphology (Academic Press, London, 1982)Google Scholar
  9. 9.
    P. Bhanu Prasad, Lecture Notes on Mathematical Morphology and Image Analysis, Lecture Notes for Training (Steel Authority of India, Ranchi, 1984)Google Scholar
  10. 10.
    P. Bhanu Prasad, C. Lantuejoul, J.P. Jernot, J.L.Chermant, unbiased estimator of Euler-Poincare characteristic. Acta Stereologica 8/2, 101–106 (1989)Google Scholar
  11. 11.
    D.T. Pham, E.J. Bayro-Corrochano, Neural classifiers for automated visual inspection. Proc. Instn. Mech. Engrs. 208, 83–89 (1994)Google Scholar
  12. 12.
    S.S. Martínez, J.G. Ortega, J.G. García, A.S. García, A machine vision system for automated headlamp lens inspection, in IEEE Conference on Automation Science and Engineering (CASE) (2011), p.157Google Scholar
  13. 13.
    M. Campos, T. Martins, M. Ferreira, C. Santos, Detection of defects in automotive metal components through computer vision, in IEEE International Symposium on Industrial Electronics, ISIE 2008 (2008)Google Scholar
  14. 14.
    D. Mery, D. Filbert, T. Jaeger, in Analytical Characterization of Aluminum and Its Alloys Jaeger, ed. by C.S. MacKenzie, G.E. Totten. Image Processing for Fault Detection in Aluminum Castings (CRC Press, Taylor and Francis, Boca Raton, 2005), pp.701–738 Google Scholar
  15. 15.
    W. Sheng, N. Xi, M. Song, Y. Chen, J.S. Rankin III, Automated CAD-guided automobile part dimensional inspection, in Proceedings of 2000 IEEE International Conference on Robotics and Automation ICRA ‘00, vol. 2Google Scholar
  16. 16.
    Automobile association,
  17. 17.
  18. 18.
  19. 19.
  20. 20.

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Pinnamaneni Bhanu Prasad
    • 1
    • 2
    Email author
  • N. Radhakrishnan
    • 1
  • S. Shankar Bharathi
    • 1
  1. 1.Rajalakshmi Engineering CollegeChennaiIndia
  2. 2.Matrix Vision GmbHOppenweilerGermany

Personalised recommendations