Arbitrarily-oriented tunnel lining defects detection from Ground Penetrating Radar images using deep Convolutional Neural networks

Wang, J, Zhang, J, Cohn, AG orcid.org/0000-0002-7652-8907 et al. (8 more authors) (2022) Arbitrarily-oriented tunnel lining defects detection from Ground Penetrating Radar images using deep Convolutional Neural networks. Automation in Construction, 133. 104044. ISSN 0926-5805

Abstract

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Authors/Creators:
Copyright, Publisher and Additional Information: © 2021 Elsevier B.V. All rights reserved. This is an author produced version of a review published in Automation in Construction. Uploaded in accordance with the publisher's self-archiving policy.
Keywords: Ground penetrating radar; Arbitrarily-oriented defect detection; Automation; Deep learning; Tunnel inspection
Dates:
  • Accepted: 6 November 2021
  • Published (online): 13 November 2021
  • Published: January 2022
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds)
Funding Information:
FunderGrant number
Alan Turing InstituteNo ref given
Depositing User: Symplectic Publications
Date Deposited: 22 Nov 2021 14:39
Last Modified: 22 Nov 2021 14:39
Status: Published
Publisher: Elsevier
Identification Number: https://doi.org/10.1016/j.autcon.2021.104044

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Filename: Arbitrarily-oriented tunnel lining defects detection from Ground_final version.pdf

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