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Machine Vision and Applications
Published in: Machine Vision and Applications 7/2016

01-10-2016 | Short Paper

A context-driven approach to image-based crack detection

Authors: Hongcheng Wang, Ziyou Xiong, Alan M. Finn, Zaffir Chaudhry

Published in: Machine Vision and Applications | Issue 7/2016

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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.
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Literature
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)CrossRef
5.
Farrar, R.R., Worden, K.: Structural Health Monitoring: A Machine Learning Perspective. Wiley, New York (2012)CrossRef
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)CrossRef
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)CrossRef
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)CrossRef
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)
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)CrossRef
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)CrossRef
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)
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)
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)
27.
Metadata
Title
A context-driven approach to image-based crack detection
Authors
Hongcheng Wang
Ziyou Xiong
Alan M. Finn
Zaffir Chaudhry
Publication date
01-10-2016
Publisher
Springer Berlin Heidelberg
Published in
Machine Vision and Applications / Issue 7/2016
Print ISSN: 0932-8092
Electronic ISSN: 1432-1769
DOI
https://doi.org/10.1007/s00138-016-0779-1

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