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dc.contributor.authorSaleem, Muhammad Rakeh
dc.contributor.authorPark, Jongwoong
dc.contributor.authorLee, Jin-Hwan
dc.contributor.authorJung, Hyung-Jo
dc.contributor.authorSarwar, Muhammad Zohaib
dc.date.accessioned2020-08-24T07:11:17Z
dc.date.available2020-08-24T07:11:17Z
dc.date.created2020-07-01T11:58:36Z
dc.date.issued2020
dc.identifier.issn1475-9217
dc.identifier.urihttps://hdl.handle.net/11250/2673476
dc.description.abstractThe structural condition of bridges is generally assessed using manual visual inspection. However, this approach consumes labor, time, and capital, and produces subjective results. Therefore, industries today are using automated visual inspection approaches, which quantify and localize damages such as cracks using robots and computer vision. This paper proposes an instant damage identification and localization approach that uses an image capturing and geo-tagging system and deep convolutional neural network for crack detection. The image capturing and geo-tagging allows the geo-tagging of three-dimensional coordinates and camera pose data with bridge inspection images; the deep convolutional neural network is trained for automated crack identification. The damages extracted by the convolutional neural network are instantly transformed into a global bridge damage map, with georeferencing data acquired using the image capturing and geo-tagging. This method is experimentally validated through a lab-scale test on a wall and a field test on a bridge to demonstrate the performance of the instant damage map.en_US
dc.language.isoengen_US
dc.publisherSageen_US
dc.titleInstant bridge visual inspection using an unmanned aerial vehicle by image capturing and geo-tagging system and deep convolutional neural networken_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionacceptedVersionen_US
dc.source.journalStructural Health Monitoringen_US
dc.identifier.doi10.1177/1475921720932384
dc.identifier.cristin1818069
dc.description.localcode© 2020. This is the authors' accepted and refereed manuscript to the article. The final authenticated version is available online at: http://journals.sagepub.com/doi/10.1177/1475921720932384en_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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