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dc.contributor.authorMirzazade, Ali
dc.contributor.authorPopescu, Cosmin
dc.contributor.authorBlanksvärd, Thomas
dc.contributor.authorTäljsten, Björn
dc.date.accessioned2021-07-09T08:24:22Z
dc.date.available2021-07-09T08:24:22Z
dc.date.created2021-07-08T15:00:19Z
dc.date.issued2021
dc.identifier.citationRemote Sensing. 2021, .
dc.identifier.issn2072-4292
dc.identifier.urihttps://hdl.handle.net/11250/2764001
dc.description.abstractFor the inspection of structures, particularly bridges, it is becoming common to replace humans with autonomous systems that use unmanned aerial vehicles (UAV). In this paper, a framework for autonomous bridge inspection using a UAV is proposed with a four-step workflow: (a) data acquisition with an efficient UAV flight path, (b) computer vision comprising training, testing and validation of convolutional neural networks (ConvNets), (c) point cloud generation using intelligent hierarchical dense structure from motion (DSfM), and (d) damage quantification. This workflow starts with planning the most efficient flight path that allows for capturing of the minimum number of images required to achieve the maximum accuracy for the desired defect size, then followed by bridge and damage recognition. Three types of autonomous detection are used: masking the background of the images, detecting areas of potential damage, and pixel-wise damage segmentation. Detection of bridge components by masking extraneous parts of the image, such as vegetation, sky,roads or rivers, can improve the 3D reconstruction in the feature detection and matching stages. In addition, detecting damaged areas involves the UAV capturing close-range images of these critical regions, and damage segmentation facilitates damage quantification using 2D images. By application of DSfM, a denser and more accurate point cloud can be generated for these detected areas, and aligned to the overall point cloud to create a digital model of the bridge. Then, this generated point cloud is evaluated in terms of outlier noise, and surface deviation. Finally, damage that has been detected is quantified and verified, based on the point cloud generated using the Terrestrial Laser Scanning (TLS) method. The results indicate this workflow for autonomous bridge inspection has potential.
dc.language.isoeng
dc.subjectBridge 3D modeling
dc.subjectBridge 3D modeling
dc.subjectDamage detection
dc.subjectDamage detection
dc.subjectBridge inspection
dc.subjectBridge inspection
dc.subjectUnmanned inspections
dc.subjectUnmanned inspections
dc.titleWorkflow for Off-Site Bridge Inspection Using Automatic Damage Detection-Case Study of the Pahtajokk Bridge
dc.typePeer reviewed
dc.typeJournal article
dc.description.versionpublishedVersion
dc.subject.nsiVDP::Teknologi: 500
dc.subject.nsiVDP::Technology: 500
dc.source.pagenumber22
dc.source.journalRemote Sensing
dc.identifier.doihttps://doi.org/10.3390/rs13142665
dc.identifier.cristin1921050
dc.relation.projectAndre: FORMAS (Sweden), project number 2019-01515
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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