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A semi-supervised self-training method to develop assistive intelligence for segmenting multiclass bridge elements from inspection videos

Author(s): (Department of Civil Engineering, Stony Brook University, Stony Brook, NY, USA)
ORCID (Department of Civil Engineering, Stony Brook University, Stony Brook, NY, USA)
ORCID (Department of Civil, Environmental and Architectural Engineering, Missouri University of Science and Technology, Rolla, MO, USA)
(Department of Biomedical Informatics, Department of Computer Science, AI Institute, Stony Brook University, Stony Brook, NY, USA)
Medium: journal article
Language(s): English
Published in: Structural Health Monitoring, , n. 3, v. 21
Page(s): 147592172110104
DOI: 10.1177/14759217211010422
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: 10.1177/14759217211010422.
  • About this
    data sheet
  • Reference-ID
    10609117
  • Published on:
    22/05/2021
  • Last updated on:
    09/05/2022
 
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