Machine Vision and Applications

, Volume 24, Issue 2, pp 227–241 | Cite as

An innovative methodology for detection and quantification of cracks through incorporation of depth perception

  • Mohammad R. Jahanshahi
  • Sami F. Masri
  • Curtis W. Padgett
  • Gaurav S. Sukhatme
Original Paper

Abstract

Visual inspection of structures is a highly qualitative method in which inspectors visually assess a structure’s condition. If a region is inaccessible, binoculars must be used to detect and characterize defects. Although several Non-Destructive Testing methods have been proposed for inspection purposes, they are nonadaptive and cannot quantify crack thickness reliably. In this paper, a contact-less remote-sensing crack detection and quantification methodology based on 3D scene reconstruction (computer vision), image processing, and pattern recognition concepts is introduced. The proposed approach utilizes depth perception to detect cracks and quantify their thickness, thereby giving a robotic inspection system the ability to analyze images captured from any distance and using any focal length or resolution. This unique adaptive feature is especially useful for incorporating mobile systems, such as unmanned aerial vehicles, into structural inspection methods since it would allow inaccessible regions to be properly inspected for cracks. Guidelines are presented for optimizing the acquisition and processing of images, thereby enhancing the quality and reliability of the damage detection approach and allowing the capture of even the slightest cracks (e.g., detection of 0.1 mm cracks from a distance of 20 m), which are routinely encountered in realistic field applications where the camera-object distance and image contrast are not controllable.

Keywords

Crack detection Thickness quantification Computer vision Image processing Pattern classification 3D scene reconstruction Morphological operation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Abas, F.S., Martinez, K.: Classification of painting cracks for content-based analysis. In: Proceedings of the SPIE—The International Society for Optical Engineering, vol. 5011, pp. 149–160. Santa Clara, CA (2003)Google Scholar
  2. 2.
    Abdel-Qader I., Abudayyeh O., Kelly M.E.: Analysis of edge-detection techniques for crack identification in bridges. J. Comput. Civ. Eng. 17(4), 255–263 (2003)CrossRefGoogle Scholar
  3. 3.
    Abdel-Qader I., Pashaie-Rad S., Abudayyeh O., Yehia S.: PCA-based algorithm for unsupervised bridge crack detection. Adv. Eng. Softw. 37(12), 771–778 (2006)CrossRefGoogle Scholar
  4. 4.
    Benning, W., Lange, J., Schwermann, R., Effkemann, C.: Monitoring crack origin and evolution at concrete elements using photogrammetry. In: Proceedings of XXth congress of ISPRS (International society for Photogrammetry and remote sensing), pp. 678–683 (2004)Google Scholar
  5. 5.
    Bouguet, J.Y.: Camera calibration toolbox for matlab. http://www.vision.caltech.edu/bouguetj/calibdoc/index.html (2008)
  6. 6.
    Chae, M.J.: Automated interpretation and assessment of sewer pipeline. Ph.D. thesis, Purdue University (2001)Google Scholar
  7. 7.
    Chen L.C., Shao Y.C., Jan H.H., Huang C.W., Tien Y.M.: Measuring system for cracks in concrete using multitemporal images. J. Surv. Eng. 132(2), 77–82 (2006)CrossRefGoogle Scholar
  8. 8.
    Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981). http://doi.acm.org/10.1145/358669.358692
  9. 9.
    Fisher R.A.: The use of multiple measurements in taxonomic problems. Ann. Eugen. 7, 179–188 (1936)CrossRefGoogle Scholar
  10. 10.
    Fujita, Y., Hamamoto, Y.: A robust automatic crack detection method from noisy concrete surfaces. Mach. Vis. Appl. 22, 245–254 (2011). doi:10.1007/s00138-009-0244-5 Google Scholar
  11. 11.
    Giakoumis I., Nikolaidis N., Pitas I.: Digital image processing techniques for the detection and removal of cracks in digitized paintings. IEEE Trans. Image Process. 15(1), 178–188 (2006)CrossRefGoogle Scholar
  12. 12.
    Jahanshahi, M.R., Masri, S.F., Sukhatme, G.S.: Multi-image stitching and scene reconstruction for evaluating defect evolution in structures. Struct. Health Monit. (2011). doi:10.1177/1475921710395809
  13. 13.
    Jahanshahi M.R., Kelly J.S., Masri S.F., Sukhatme G.S.: A survey and evaluation of promising approaches for automatic image-based defect detection of bridge structures. Struct. Infrastruct. Eng. 5(6), 455–486 (2009). doi:10.1080/15732470801945930 CrossRefGoogle Scholar
  14. 14.
    Kaseko M.S., Lo Z.P., Ritchie S.G.: Comparison of traditional and neural classifiers for pavement-crack detection. J. Transp. Eng. 120(4), 552–569 (1994)CrossRefGoogle Scholar
  15. 15.
    Lowe D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Compu. Vis. 60(2), 91–110 (2004)CrossRefGoogle Scholar
  16. 16.
    Matheron G.: Random Sets and Integral Geometry. Wiley, New York (1975)MATHGoogle Scholar
  17. 17.
    McCrea A., Chamberlain D., Navon R.: Automated inspection and restoration of steel bridges—a critical review of methods and enabling technologies. Autom. Constr. 11(4), 351–373 (2002)CrossRefGoogle Scholar
  18. 18.
    Minkowski H.: Volumen und oberfläche. Math. Ann. 57(4), 447–495 (1903)MathSciNetMATHCrossRefGoogle Scholar
  19. 19.
    Moselhi O., Shehab-Eldeen T.: Classification of defects in sewer pipes using neural networks. J. Infrastruct. Syst. 6(3), 97–104 (2000)CrossRefGoogle Scholar
  20. 20.
    Nieniewski M., Chmielewski L., Jozwik A., Sklodowski M.: Morphological detection and feature-based classification of cracked regions in ferrites. Mach. Graph. Vis. 8(4), 699–712 (1999)Google Scholar
  21. 21.
    Otsu N.: A threshold selection method from gray-level histogrmas. IEEE Trans. Syst. Man. Cybern. 9(1), 62–66 (1979)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Salembier, P.: Comparison of some morphological segmentation algorithms based on contrast enhancement. Application to automatic defect detection. In: Proceedings of the EUSIPCO-90—Fifth European Signal Processing Conference, pp. 833–836 (1990)Google Scholar
  23. 23.
    Siegel, M., Gunatilake, P.: Remote enhanced visual inspection of aircraft by a mobile robot. In: IEEE Workshop on Emerging Technologies, Intelligent Measurement and Virtual Systems for Instrumentation and Measurement—ETIMVIS’98. St. Paul, MN (1998)Google Scholar
  24. 24.
    Sinha S.K., Fieguth P.W.: Morphological segmentation and classification of underground pipe images. Mach. Vis. Appl. 17(1), 21–31 (2006)CrossRefGoogle Scholar
  25. 25.
    Sinha S.K., Fieguth P.W., Polak M.A.: Computer vision techniques for automatic structural assessment of underground pipes. Comput. Aided Civ. Infrastruct. Eng 18(2), 95–112 (2003)CrossRefGoogle Scholar
  26. 26.
    Snavely, K.N.: Scene reconstruction and visualization from internet photo collections. Ph.D. thesis, University of Washington, Seattle, Washington, USA (2008)Google Scholar
  27. 27.
    Snavely, N., Seitz, S.M., Szeliski, R.: Photo tourism: exploring photo collections in 3D. In: SIGGRAPH Conference Proceedings, pp. 835–846. ACM Press, New York (2006)Google Scholar
  28. 28.
    Tsao S., Kehtarnavaz N., Chan P., Lytton R.: Image-based expert-system approach to distress detection on CRC pavement. J. Transp. Eng. 120(1), 62–64 (1994)CrossRefGoogle Scholar
  29. 29.
    Wang, K.C., Nallamothu, S., Elliott, R.P.: Classification of pavement surface distress with an embedded neural net chip. In: Artificial Neural Networks for Civil Engineers: Advanced Features and Applications, pp. 131–161. ASCE, (1998)Google Scholar
  30. 30.
    Yamaguchi T., Hashimoto S.: Fast crack detection method for large-size concrete surface images using percolation-based image processing. Mach. Vis. Appl. 21, 797–809 (2010). doi:10.1007/s00138-009-0189-8 CrossRefGoogle Scholar
  31. 31.
    Yu S.N., Jang J.H., Han C.S.: Auto inspection system using a mobile robot for detecting concrete cracks in a tunnel. Autom. Constr. 16(3), 255–261 (2007)CrossRefGoogle Scholar
  32. 32.
    Zhu, Z., German, S., Brilakis, I.: Visual retrieval of concrete crack properties for automated post-earthquake structural safety evaluation. Autom. Constr. 20(7), 874–883 (2011). doi:10.1016/j.autcon.2011.03.004; http://www.sciencedirect.com/science/article/pii/S0926580511000318

Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • Mohammad R. Jahanshahi
    • 1
  • Sami F. Masri
    • 1
  • Curtis W. Padgett
    • 2
  • Gaurav S. Sukhatme
    • 3
  1. 1.Sonny Astani Department of Civil and Environmental EngineeringUniversity of Southern CaliforniaLos AngelesUSA
  2. 2.Jet Propulsion LaboratoryPasadenaUSA
  3. 3.Computer Science DepartmentUniversity of Southern CaliforniaLos AngelesUSA

Personalised recommendations