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CrackDenseLinkNet: a deep convolutional neural network for semantic segmentation of cracks on concrete surface images

Author(s): ORCID (Department of Civil and Environmental Engineering, University of Southern California, Los Angeles, CA, USA)
(Department of Civil and Environmental Engineering, University of Southern California, Los Angeles, CA, USA)
(Collaboratory for Advanced Computing and Simulations, Department of Computer Science, Department of Physics and Astronomy, and Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA)
(Department of Civil and Environmental Engineering, University of Southern California, Los Angeles, CA, USA)
Medium: journal article
Language(s): English
Published in: Structural Health Monitoring, , n. 2, v. 23
Page(s): 147592172311733
DOI: 10.1177/14759217231173305
Structurae cannot make the full text of this publication available at this time. The full text can be accessed through the publisher via the DOI: 10.1177/14759217231173305.
  • About this
    data sheet
  • Reference-ID
    10730094
  • Published on:
    30/05/2023
  • Last updated on:
    15/03/2024
 
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