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

Content Provider	Springer Nature Link
Author	Jahanshahi, Mohammad R. Masri, Sami F. Padgett, Curtis W. Sukhatme, Gaurav S.
Copyright Year	2011
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.
Starting Page	227
Ending Page	241
Page Count	15
File Format	PDF
ISSN	09328092
Journal	Machine Vision and Applications
Volume Number	24
Issue Number	2
e-ISSN	14321769
Language	English
Publisher	Springer-Verlag
Publisher Date	2011-12-27
Publisher Place	Berlin/Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Crack detection Thickness quantification Computer vision Image processing Pattern classification 3D scene reconstruction Morphological operation Pattern Recognition Image Processing and Computer Vision Communications Engineering, Networks
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Computer Science Applications Software Hardware and Architecture

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