Skip to main content
SpringerLink
Log in

A context-driven approach to image-based crack detection

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

Abstract

We present a novel context-driven approach to image-based crack detection for automated inspection of aircraft surface and subsurface defects. In contrast to existing image-based crack detection methods, which rely mostly on low-level image processing and data-driven methods, our method explicitly incorporates multiple high-level context into low-level processing. We present two classes of context: geometric/structural context and physical context. We formulate mathematically a sparse decomposition problem to incorporate the context and apply robust principal component analysis to decompose typical repetitive rivet regions into a normal component and a sparse component. Cracks are detected in the sparse component. By applying the proposed context-driven approach to coated and uncoated test specimens, we achieve significant reduction in false detections compared to the approach without exploiting context.

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

Similar content being viewed by others

References

  1. Candès, E., Li, X., Ma, Y., Wright, J.: Robust principal component analysis? J. ACM 58(3), 1–37 (2011)

  2. Cević, I.J., Orteu, J.-J., Sentenac, T., Gilblas, R.: Automated visual inspection of an airplane exterior. In:Proceedings of the SPIE 9534, Twelfth International Conference on Quality Control by Artificial Vision, vol. 9534 (2015)

  3. Chambon, S., Moliard, J.: Automatic road pavement assessment with image processing: review and comparison. Int. J. Geophys. 2011, 1–28 (2011)

  4. Dai-Heng, C., Hironobu, N.: Detection of a crack by body force method. Eng. Fract. Mech. 45(5), 671–685 (1993)

    Article  Google Scholar 

  5. Farrar, R.R., Worden, K.: Structural Health Monitoring: A Machine Learning Perspective. Wiley, New York (2012)

    Book  Google Scholar 

  6. Gordon, R.O.: 43-204—Visual Inspection for Aircraft. AFS-350, U.S. Department of Transportation (1997)

  7. Gunatilake, P., Siegel, M., Jordan, A., Podnar, G.: Image understanding algorithms for remote visual inspection of aircraft surfaces. In: Machine Vision Applications in Industrial Inspection V, vol. 3029, pp. 2 – 13 (1997)

  8. Heoppner, D.W.: The Role of Fretting Fatigue on Aircraft Rivet Hole Cracking. Federal Aviation Administration, Office of Aviation Research (1996)

  9. Illingworth, J., Kittler, J.: Survey of the hough transform. Comput. Vis. Graph. Image Process. 44(1), 87–116 (1988)

    Article  Google Scholar 

  10. Jahanshahi, M.R., Kelly, J.S., Masri, S.F., Sukhatme, G.S.: Survey and evaluation of promising approaches for automatic image-based defect detection of bridge structures. Struct. Infrastruct. Eng. 5(6), 455–486 (2009)

    Article  Google Scholar 

  11. Jahanshahi, M.R., Masri, S.F., Padgett, C.W., Sukhatme, G.S.: An innovative methodology for detection and quantification of cracks through incorporation of depth perception. Mach. Vis. Appl. 24(2), 227–241 (2013)

    Article  Google Scholar 

  12. Lin, Z., Chen, M., Ma, Y.: The Augmented Lagrange Multiplier Method for Exact Recovery of Corrupted Low-Rank Matrices. UIUC Tech. Report UILU-ENG-09-2214 (2010)

  13. Lowe, D.G.: Object recognition from local scale-invariant features.In: Proceedings of the International Conference on Computer Vision, pp. 1150–1157 (1993)

  14. Mumtaz, R., Mumtaz, M., Mansoor, A.B., Masood, H.: Computer aided visual inspection of aircraft surfaces. Int. J. Image Process. 6(1), 38–53 (2012)

    Google Scholar 

  15. Nguyen, H., Kam, T.Y., Cheng, P.Y.: Crack image extraction using a radial basis functions based level set interpolation technique. In: IEEE International Conference on Computer Science and Electronics Engineering (ICCSEE), pp. 118–122 (2012)

  16. Oliveira, H., Correia, P.: Automatic road crack detection and characterization. IEEE Trans. Intell. Transp. Syst. 14(1), 155–168 (2013)

    Article  Google Scholar 

  17. Schmugge, S.J., Rice, L., Nguyen, N.R., Lindbergy, J., Grizziy, R., Joffey, C., Shin, M.C.: Detection of cracks in nuclear power plant using spatial-temporal grouping of local patches. In: Proceedings of the WACV 2016, IEEE Winter Conference on Applications of Computer Vision (2016)

  18. Scholz-Reiter, B., Weimer, D., Thamer, H.: Automated surfaceinspection of cold-formed micro-parts. CIRP Ann.Manuf. Technol. 61, 531–534 (2012)

    Article  Google Scholar 

  19. Shahraray, B., Schmidt, A., Palmquist, J.: Defect detection, classification and quantification in optical fiber connectors. In: IAPR Workshop on Machine Vision Applications, pp. 15–22 (1990)

  20. Siegel, M., Gunatilake, P.: Enhanced remote visual inspection of aircraft skin. In: Intelligent NDE Sciences for Aging and Futuristic Aircraft (1997)

  21. Staszewski, W., Boller, C., Tomlinson, G.: Health Monitoring of Aerospace Structures: Smart Sensor Technologies and Signal Processing. Wiley, New York (2004)

    Google Scholar 

  22. Strat, T.M.: Employing contextual information in computer vision. In: ARPA Image Understanding Workshop (1993)

  23. Sumi, N., Taguchi, R., Hattori, K., Hoguro, M., Umezaki, T.: Automatic visual inspection system of fasteners for aircraft using 3d measuring. J. Jpn. Soc. Precis. Eng. 81(12), 1140–1145 (2015)

  24. Sutton, M.A., Orteu, J.-J., Schreier, H.W.: Image Correlation for Shape, Motion and Deformation Measurements: Basic Concepts, Theory and Applications. Springer, Berlin (2009)

    Google Scholar 

  25. Weimer, D., Thamer, H., Scholz-Reiter, B.: Learning defectclassifiers for textured surfaces using neural networks andstatistical feature representations. In: 46th CIRP Conference on Manufacturing Systems, pp. 347–352 (2013)

  26. Yamaguchi, T., Hashimoto, S.: Fast crack detection method for large-size concrete surface images using percolation-based image processing. Mach. Vis. Appl. Special Issue Integr. Imaging Vis. Tech. Ind. Insp. 21(5), 797–809 (2010)

    Google Scholar 

  27. Zhang, Z., Ganesh, A., Liang, X., Ma, Y.: TILT: transform invariant low-rank textures. Int. J. Comput. Vis. 99(1), 1–24 (2012)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Comcast Labs, 1110 Vermont Ave NW, Washington, DC, 20005, USA

    Hongcheng Wang

  2. United Technologies Research Center (UTRC), 411 Silver Ln, East Hartford, CT, 06118, USA

    Ziyou Xiong, Alan M. Finn & Zaffir Chaudhry

Authors
  1. Hongcheng Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ziyou Xiong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alan M. Finn
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zaffir Chaudhry
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hongcheng Wang.

Additional information

The work was done while the first author was affiliated with United Technologies Research Center (UTRC), East Hartford, Connecticut, USA.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, H., Xiong, Z., Finn, A.M. et al. A context-driven approach to image-based crack detection. Machine Vision and Applications 27, 1103–1114 (2016). https://doi.org/10.1007/s00138-016-0779-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00138-016-0779-1

Keywords

Search

Navigation