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Benchmarking Image Processing Algorithms for Unmanned Aerial System-Assisted Crack Detection in Concrete Structures

1
Office of Infrastructure Research and Development, Turner-Fairbank Highway Research Center, Federal Highway Administration, VA 22101, USA
2
Department of Civil and Environmental Engineering, Clarkson University, Potsdam, NY 13699-5710, USA
3
Department of Civil and Environmental Engineering, Utah State University, Logan, UT 84322-4110, USA
*
Author to whom correspondence should be addressed.
Infrastructures 2019, 4(2), 19; https://doi.org/10.3390/infrastructures4020019
Received: 24 March 2019 / Revised: 23 April 2019 / Accepted: 29 April 2019 / Published: 30 April 2019
(This article belongs to the Special Issue Intelligent Infrastructures)
PDF [2156 KB, uploaded 30 April 2019]

Abstract

This paper summarizes the results of traditional image processing algorithms for detection of defects in concrete using images taken by Unmanned Aerial Systems (UASs). Such algorithms are useful for improving the accuracy of crack detection during autonomous inspection of bridges and other structures, and they have yet to be compared and evaluated on a dataset of concrete images taken by UAS. The authors created a generic image processing algorithm for crack detection, which included the major steps of filter design, edge detection, image enhancement, and segmentation, designed to uniformly compare different edge detectors. Edge detection was carried out by six filters in the spatial (Roberts, Prewitt, Sobel, and Laplacian of Gaussian) and frequency (Butterworth and Gaussian) domains. These algorithms were applied to fifty images each of defected and sound concrete. Performances of the six filters were compared in terms of accuracy, precision, minimum detectable crack width, computational time, and noise-to-signal ratio. In general, frequency domain techniques were slower than spatial domain methods because of the computational intensity of the Fourier and inverse Fourier transformations used to move between spatial and frequency domains. Frequency domain methods also produced noisier images than spatial domain methods. Crack detection in the spatial domain using the Laplacian of Gaussian filter proved to be the fastest, most accurate, and most precise method, and it resulted in the finest detectable crack width. The Laplacian of Gaussian filter in spatial domain is recommended for future applications of real-time crack detection using UAS.
Keywords: Structural condition assessment; concrete structures; unmanned aerial systems; crack detection; image processing; noncontact methods Structural condition assessment; concrete structures; unmanned aerial systems; crack detection; image processing; noncontact methods
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Dorafshan, S.; Thomas, R.J.; Maguire, M. Benchmarking Image Processing Algorithms for Unmanned Aerial System-Assisted Crack Detection in Concrete Structures. Infrastructures 2019, 4, 19.

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