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

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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.

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Authors and Affiliations

  1. Sonny Astani Department of Civil and Environmental Engineering, University of Southern California, Los Angeles, CA, 90089, USA

    Mohammad R. Jahanshahi & Sami F. Masri

  2. Jet Propulsion Laboratory, Pasadena, CA, 91109, USA

    Curtis W. Padgett

  3. Computer Science Department, University of Southern California, Los Angeles, CA, 90089, USA

    Gaurav S. Sukhatme

Authors
  1. Mohammad R. Jahanshahi
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  2. Sami F. Masri
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  3. Curtis W. Padgett
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  4. Gaurav S. Sukhatme
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Correspondence to Mohammad R. Jahanshahi.

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Jahanshahi, M.R., Masri, S.F., Padgett, C.W. et al. An innovative methodology for detection and quantification of cracks through incorporation of depth perception. Machine Vision and Applications 24, 227–241 (2013). https://doi.org/10.1007/s00138-011-0394-0

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  • DOI: https://doi.org/10.1007/s00138-011-0394-0

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