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Autonomous detection of damage to multiple steel surfaces from 360° panoramas using deep neural networks

Author(s): (College of Oceanography and Space Informatics China University of Petroleum (East China) Qingdao China)
(Department of Design, Manufacturing & Engineering Management University of Strathclyde Glasgow UK)
(School of Mechanical Engineering Xi''an Jiaotong University Shaanxi China)
(College of Oceanography and Space Informatics China University of Petroleum (East China) Qingdao China)
(College of Oceanography and Space Informatics China University of Petroleum (East China) Qingdao China)
(School of Computer Science and Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing China)
(Department of Design, Manufacturing & Engineering Management University of Strathclyde Glasgow UK)
(College of Mechanical and Electronic Engineering China University of Petroleum (East China) Qingdao China)
Medium: journal article
Language(s): English
Published in: Computer-Aided Civil and Infrastructure Engineering, , n. 12, v. 36
Page(s): 1585-1599
DOI: 10.1111/mice.12686
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.12686.
  • About this
    data sheet
  • Reference-ID
    10601280
  • Published on:
    17/04/2021
  • Last updated on:
    29/11/2021
 
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