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    Training Deep Learning Segmentation Models Using Super-Resolution Crack Images for Detection of Thin Concrete Cracks

    Source: Journal of Computing in Civil Engineering:;2025:;Volume ( 039 ):;issue: 004::page 04025035-1
    Author:
    Oh, Dokyoon
    ,
    Jeong, Seran
    ,
    Bae, Suk-Kyoung
    ,
    Kim, Byunghyun
    ,
    Cho, Soojin
    DOI: 10.1061/JCCEE5.CPENG-6240
    Publisher: American Society of Civil Engineers
    Abstract: AbstractCracks are the most critical damage types on concrete structures, and they are commonly assessed based on visual inspection. Recently, there have been many attempts to replace conventional inspection with computer vision-based inspection powered ...
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      Training Deep Learning Segmentation Models Using Super-Resolution Crack Images for Detection of Thin Concrete Cracks

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    https://yetl.yabesh.ir/yetl1/handle/yetl/4314366
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    contributor authorOh, Dokyoon
    contributor authorJeong, Seran
    contributor authorBae, Suk-Kyoung
    contributor authorKim, Byunghyun
    contributor authorCho, Soojin
    date accessioned2026-08-20T21:22:54Z
    date available2026-08-20T21:22:54Z
    date copyright2025/03/31
    date issued2025
    identifier otherJCCEE5.CPENG-6240.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4314366
    description abstractAbstractCracks are the most critical damage types on concrete structures, and they are commonly assessed based on visual inspection. Recently, there have been many attempts to replace conventional inspection with computer vision-based inspection powered ...
    publisherAmerican Society of Civil Engineers
    titleTraining Deep Learning Segmentation Models Using Super-Resolution Crack Images for Detection of Thin Concrete Cracks
    typeJournal Article
    journal volume39
    journal issue4
    journal titleJournal of Computing in Civil Engineering
    identifier doi10.1061/JCCEE5.CPENG-6240
    journal fristpage04025035-1
    journal lastpage04025035-14
    page14
    treeJournal of Computing in Civil Engineering:;2025:;Volume ( 039 ):;issue: 004
    contenttypeFulltext
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