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

dc.contributor.authorHou, Jia
dc.contributor.authorChen, Wei
dc.contributor.authorCai, Lixiong
dc.contributor.authorLi, Hang
dc.contributor.authorYu, Mingyu
dc.date.accessioned2026-08-20T21:28:17Z
dc.date.available2026-08-20T21:28:17Z
dc.date.copyright2026/02/03
dc.date.issued2026
dc.identifier.otherJCCEE5.CPENG-7290.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4314509
dc.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 ...
dc.publisherAmerican Society of Civil Engineers
dc.titleOptimizing Semantic Segmentation of Corrosion Defects with Coordinated Attention and Feature Integration
dc.typeJournal Article
dc.journal.volume40
dc.journal.issue3
dc.journal.titleJournal of Computing in Civil Engineering
dc.identifier.doi10.1061/JCCEE5.CPENG-7290
dc.journal.fristpage04026017-1
dc.journal.lastpage04026017-15
dc.page15
dc.treeJournal of Computing in Civil Engineering:;2026:;Volume ( 040 ):;issue: 003
dc.contenttypeFulltext


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