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An active learning framework featured Monte Carlo dropout strategy for deep learning-based semantic segmentation of concrete cracks from images

Auteur(s): (Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, HKSAR, China)
(Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, HKSAR, China)
(Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, HKSAR, China)
(Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, HKSAR, China)
(Shenzhen Key Laboratory of Intelligent Structure System in Civil Engineering, Harbin Institute of Technology, Shenzhen, China)
ORCID (Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, HKSAR, China)
Médium: article de revue
Langue(s): anglais
Publié dans: Structural Health Monitoring, , n. 5, v. 22
Page(s): 147592172211503
DOI: 10.1177/14759217221150376
Structurae ne peut pas vous offrir cette publication en texte intégral pour l'instant. Le texte intégral est accessible chez l'éditeur. DOI: 10.1177/14759217221150376.
  • Informations
    sur cette fiche
  • Reference-ID
    10714754
  • Publié(e) le:
    21.03.2023
  • Modifié(e) le:
    01.09.2023
 
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