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Automatic Crack Segmentation for UAV-Assisted Bridge Inspection

Ayele, YZ; Aliyari, M; Griffiths, D; Droguett, EL; (2020) Automatic Crack Segmentation for UAV-Assisted Bridge Inspection. Energies , 13 (23) p. 6250. 10.3390/en13236250. Green open access

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Bridges are a critical piece of infrastructure in the network of road and rail transport system. Many of the bridges in Norway (in Europe) are at the end of their lifespan, therefore regular inspection and maintenance are critical to ensure the safety of their operations. However, the traditional inspection procedures and resources required are so time consuming and costly that there exists a significant maintenance backlog. The central thrust of this paper is to demonstrate the significant benefits of adapting a Unmanned Aerial Vehicle (UAV)-assisted inspection to reduce the time and costs of bridge inspection and established the research needs associated with the processing of the (big) data produced by such autonomous technologies. In this regard, a methodology is proposed for analysing the bridge damage that comprises three key stages, (i) data collection and model training, where one performs experiments and trials to perfect drone flights for inspection using case study bridges to inform and provide necessary (big) data for the second key stage, (ii) 3D construction, where one built 3D models that offer a permanent record of element geometry for each bridge asset, which could be used for navigation and control purposes, (iii) damage identification and analysis, where deep learning-based data analytics and modelling are applied for processing and analysing UAV image data and to perform bridge damage performance assessment. The proposed methodology is exemplified via UAV-assisted inspection of Skodsberg bridge, a 140 m prestressed concrete bridge, in the Viken county in eastern Norway.

Type: Article
Title: Automatic Crack Segmentation for UAV-Assisted Bridge Inspection
Open access status: An open access version is available from UCL Discovery
DOI: 10.3390/en13236250
Publisher version: https://doi.org/10.3390/en13236250
Language: English
Additional information: This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
Keywords: drone-assisted bridge inspection; crack detection; crack segmentation; damage assessment; UAV; performance analysis
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10116545
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