Skip to main content
SpringerLink
Log in

Defect identification on specular machined surfaces

  • Original Paper
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract

In many industrial applications, it is important to identify defects on specular surfaces. On machined surfaces, defect identification may be further complicated by the presence of marks from a machining process. These marks may dramatically and unpredictably change the appearance of the surface, while not altering its ability to function. To differentiate between surface characteristics that constitute a defect and those that do not, we propose a system that directly illuminates specular machined surfaces with a programmable array of high-power light-emitting diodes that allows the angle of the incident light to be varied over a series of images. A reflection model is used to predict the reflected intensity as a function of incident lighting angle for each point on the imaged surface. A surface defect causes the observed reflected intensity as a function of incident lighting angle to differ from that predicted by the reflection model. Such differences between the observations and the reflection model are shown to identify surface defects such as porosities, dents and scratches in the presence of marks from the machining process.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  1. Sylla, C.: Experimental investigation of human and machine-vision arrangements in inspection tasks. Control Eng. Pract. 10(3), 347–361 (2002)

    Article  Google Scholar 

  2. Yang, K.: Adaptive machine vision for automotive component inspection. M.A.Sc. thesis, Dept. of Mech. Eng., McMaster University, Hamilton, Canada (2008)

  3. Ma, W., Hawkins, T., Peers, P., Chabert, C., Weiss, M., Debevec, P.: Rapid acquisition of specular and diffuse normal maps from polarized spherical gradient illumination. Eurograph. Symp. Render. 2007(9), 183–194 (2007)

  4. Li, C., Zhang, Z., Miyaki, T., Imamura, T., Fujiwa, H.: Processing specular reflection components of chrome-plated surface by multi-image reconstruction method. Int. J. Comput. Inf. Syst. Ind. Manage. 1, 303–311 (2009)

    Google Scholar 

  5. Varun, A.V.: Adaptive lighting for machine vision applications. 2011 Canadian Conference on Computer and Robot Vision, pp. 140–145 (2011)

  6. Seulin, R., Merienne, F., Gorria, P.: Dynamic lighting system for specular surface inspection. SPIE Mach. Vis. Appl. Ind. Insp. VII 4301, 196–202 (2001)

    Google Scholar 

  7. Aluze, D., Merienne, F., Dumont, C., Gorria, P.: Vision system for defect imaging, detection, and characterization on a specular surface of a 3D object. Image Vis. Comput. 20(8), 569–580 (2002)

    Article  Google Scholar 

  8. Morel, O., Stolz, C., Meriaudeau, F., Gorria, P.: Active lighting applied to three-dimensional reconstruction of specular metallic surfaces by polarization imaging. Appl. Opt. 45(17), 4062–4068 (2006)

    Article  Google Scholar 

  9. Salis, G., Seulin, R., Morel, O., Meriaudeau, F.: Machine vision system for the inspection of reflective parts in the automotive industry. Proc. SPIE 6503, 65030O (2007)

    Article  Google Scholar 

  10. Sills, K., Capson, D., Bone, G.: Specular-reduced imaging for inspection of machined surfaces. In: Ninth Conference on Computer and Robot Vision, Toronto, Canada, pp. 361–368 (2012)

  11. Feris, R.S., Turk, M., Raskar, R., Tan, K.: Specular highlights detection and reduction with multi-flash photography. Int. J. Braz. Comput. Soc. 1(12), 35–42 (2006)

    Google Scholar 

  12. Pernkopf, F., O’Leary, P.: Visual inspection of machined metallic high-precision surfaces. EURASIP J. Adv. Sig. Proc. 7, 667–678 (2002)

    Article  Google Scholar 

  13. Torrance, K., Sparrow, E.: Theory for off-specular reflection from roughened surfaces. J. Opt. Soc. Am. 57(9), 1105–1114 (1967)

    Article  Google Scholar 

  14. Nayar, S., Ikeuchi, D., Kanade, T.: Surface reflection: physical and geometrical perspectives. IEEE Trans. Patten Anal. Mach. Intell. 13(7), 611–634 (1991)

    Article  Google Scholar 

  15. Beckman, P., Spizzochino, A.: The Scattering of Electromagnetic Waves from Rough Surfaces. Pergamon, Oxford (1963)

    Google Scholar 

  16. Kierkegaard, P.: Reflection properties of machined metal surfaces. Opt. Eng. 35(3), 845–857 (1996)

    Article  Google Scholar 

  17. Robertson, M., Borman, S., Stevenson, R.: Dynamic range improvement through multiple exposures. In: International Conference on Image Processing, Kobe, Japan vol. 3, pp. 159–163 (1999)

  18. Chiu, S.-N., Perng, M.-H.: Reflection-area-based feature descriptor for solder joint inspection. Mach. Vis. Appl. 18(2), 95–106 (2007)

    Article  Google Scholar 

  19. Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. SMC–9(1), 62–66 (1979)

    MathSciNet  Google Scholar 

  20. Chow, C.K., Kaneko, T.: Automatic boundary detection of the left ventricle from cineangiograms. Comput. Biomed. Res. 5, 338–410 (1972)

    Article  Google Scholar 

  21. Chang, D.C., Wu, W.R.: Image contrast enhancement based on a local standard deviation model. IEEE Nucl. Sci. Symp. Med. Imaging Conf. 3, 1826–1830 (1996)

    Google Scholar 

Download references

Acknowledgments

The authors are grateful for financial support from the Natural Sciences & Engineering Research Council of Canada (NSERC).

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, McMaster University, Hamilton, Canada

    Ken Sills

  2. Department of Mechanical Engineering, McMaster University, Hamilton, Canada

    Gary M. Bone

  3. Department of Electrical and Computer Engineering, University of Victoria, Victoria, Canada

    David Capson

Authors
  1. Ken Sills
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gary M. Bone
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. David Capson
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ken Sills.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (mpg 4596 KB)

Supplementary material 2 (mpg 4445 KB)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Sills, K., Bone, G.M. & Capson, D. Defect identification on specular machined surfaces. Machine Vision and Applications 25, 377–388 (2014). https://doi.org/10.1007/s00138-013-0590-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00138-013-0590-1

Keywords

Search

Navigation