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    Optimizing Semantic Segmentation of Corrosion Defects with Coordinated Attention and Feature Integration

    Source: Journal of Computing in Civil Engineering:;2026:;Volume ( 040 ):;issue: 003::page 04026017-1
    Author:
    Hou, Jia
    ,
    Chen, Wei
    ,
    Cai, Lixiong
    ,
    Li, Hang
    ,
    Yu, Mingyu
    DOI: 10.1061/JCCEE5.CPENG-7290
    Publisher: American Society of Civil Engineers
    Abstract: AbstractThe corrosion segmentation technique powered by deep learning enables early detection and timely intervention, effectively preventing further progression and ensuring the safety and extended service life of steel structures. To address the ...
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      Optimizing Semantic Segmentation of Corrosion Defects with Coordinated Attention and Feature Integration

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    https://yetl.yabesh.ir/yetl1/handle/yetl/4314509
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    contributor authorHou, Jia
    contributor authorChen, Wei
    contributor authorCai, Lixiong
    contributor authorLi, Hang
    contributor authorYu, Mingyu
    date accessioned2026-08-20T21:28:17Z
    date available2026-08-20T21:28:17Z
    date copyright2026/02/03
    date issued2026
    identifier otherJCCEE5.CPENG-7290.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4314509
    description abstractAbstractThe corrosion segmentation technique powered by deep learning enables early detection and timely intervention, effectively preventing further progression and ensuring the safety and extended service life of steel structures. To address the ...
    publisherAmerican Society of Civil Engineers
    titleOptimizing Semantic Segmentation of Corrosion Defects with Coordinated Attention and Feature Integration
    typeJournal Article
    journal volume40
    journal issue3
    journal titleJournal of Computing in Civil Engineering
    identifier doi10.1061/JCCEE5.CPENG-7290
    journal fristpage04026017-1
    journal lastpage04026017-15
    page15
    treeJournal of Computing in Civil Engineering:;2026:;Volume ( 040 ):;issue: 003
    contenttypeFulltext
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