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Hybrid deep learning architecture for rail surface segmentation and surface defect detection

Author(s): (State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China)
(State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China)
(Department of Civil and Environmental Engineering University of South Carolina Columbia South Carolina USA)
(Department of Civil and Environmental Engineering University of South Carolina Columbia South Carolina USA)
(State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China)
(State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China)
Medium: journal article
Language(s): English
Published in: Computer-Aided Civil and Infrastructure Engineering, , n. 2, v. 37
Page(s): 227-244
DOI: 10.1111/mice.12710
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: 10.1111/mice.12710.
  • About this
    data sheet
  • Reference-ID
    10612567
  • Published on:
    09/07/2021
  • Last updated on:
    07/01/2022
 
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