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      Concrete Crack Identification Using a UAV Incorporating Hybrid Image Processing

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          Abstract

          Crack assessment is an essential process in the maintenance of concrete structures. In general, concrete cracks are inspected by manual visual observation of the surface, which is intrinsically subjective as it depends on the experience of inspectors. Further, it is time-consuming, expensive, and often unsafe when inaccessible structural members are to be assessed. Unmanned aerial vehicle (UAV) technologies combined with digital image processing have recently been applied to crack assessment to overcome the drawbacks of manual visual inspection. However, identification of crack information in terms of width and length has not been fully explored in the UAV-based applications, because of the absence of distance measurement and tailored image processing. This paper presents a crack identification strategy that combines hybrid image processing with UAV technology. Equipped with a camera, an ultrasonic displacement sensor, and a WiFi module, the system provides the image of cracks and the associated working distance from a target structure on demand. The obtained information is subsequently processed by hybrid image binarization to estimate the crack width accurately while minimizing the loss of the crack length information. The proposed system has shown to successfully measure cracks thicker than 0.1 mm with the maximum length estimation error of 7.3%.

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          Unmanned aerial systems for photogrammetry and remote sensing: A review

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            Deep Learning-Based Crack Damage Detection Using Convolutional Neural Networks

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              UAV for 3D mapping applications: a review

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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                07 September 2017
                September 2017
                : 17
                : 9
                : 2052
                Affiliations
                [1 ]School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Korea; guswns3@ 123456unist.ac.kr (H.K.); lee.junhwa@ 123456unist.ac.kr (J.L.); eunjong@ 123456unist.ac.kr (E.A.); msshin@ 123456unist.ac.kr (M.S.)
                [2 ]Department of Civil Engineering, University of Seoul, Seoul 02504, Korea; soojin@ 123456uos.ac.kr
                Author notes
                [* ]Correspondence: ssim@ 123456unist.ac.kr ; Tel.: +82-52-217-2816
                Author information
                https://orcid.org/0000-0003-3838-4153
                https://orcid.org/0000-0002-7737-1892
                Article
                sensors-17-02052
                10.3390/s17092052
                5621440
                28880254
                6e9a7b29-ebf3-4c66-86c9-c195e26d1072
                © 2017 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 10 August 2017
                : 06 September 2017
                Categories
                Article

                Biomedical engineering
                concrete structure,crack identification,digital image processing,structural health monitoring,unmanned aerial vehicle

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