Original Paper

Machine Vision and Applications

, Volume 24, Issue 2, pp 227-241

First online:

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

  • Mohammad R. JahanshahiAffiliated withSonny Astani Department of Civil and Environmental Engineering, University of Southern California Email author 
  • , Sami F. MasriAffiliated withSonny Astani Department of Civil and Environmental Engineering, University of Southern California
  • , Curtis W. PadgettAffiliated withJet Propulsion Laboratory
  • , Gaurav S. SukhatmeAffiliated withComputer Science Department, University of Southern California

Rent the article at a discount

Rent now

* Final gross prices may vary according to local VAT.

Get Access


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.


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