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Deep learning and image processing for automated crack detection and defect measurement in underground structures

Panella, F; Boehm, J; Loo, Y; Kaushik, A; Gonzalez, D; (2018) Deep learning and image processing for automated crack detection and defect measurement in underground structures. In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. (pp. pp. 829-835). ISPRS SC: Riva del Garda, Italy. Green open access

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Abstract

This work presents the combination of Deep-Learning (DL) and image processing to produce an automated cracks recognition and defect measurement tool for civil structures. The authors focus on tunnel civil structures and survey and have developed an end to end tool for asset management of underground structures. In order to maintain the serviceability of tunnels, regular inspection is needed to assess their structural status. The traditional method of carrying out the survey is the visual inspection: simple, but slow and relatively expensive and the quality of the output depends on the ability and experience of the engineer as well as on the total workload (stress and tiredness may influence the ability to observe and record information). As a result of these issues, in the last decade there is the desire to automate the monitoring using new methods of inspection. The present paper has the goal of combining DL with traditional image processing to create a tool able to detect, locate and measure the structural defect.

Type: Proceedings paper
Title: Deep learning and image processing for automated crack detection and defect measurement in underground structures
Event: ISPRS TC II Mid-term Symposium “Towards Photogrammetry 2020”
Open access status: An open access version is available from UCL Discovery
DOI: 10.5194/isprs-archives-XLII-2-829-2018
Publisher version: https://doi.org/10.5194/isprs-archives-XLII-2-829-...
Language: English
Additional information: © Author(s) 2018. This work is distributed under the Creative Commons Attribution 4.0 License.
Keywords: Deep Learning, Automated Crack Detection, Photographic Tunnelling Surveys
UCL classification: UCL > Provost and Vice Provost Offices
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: http://discovery.ucl.ac.uk/id/eprint/10051437
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