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Enhanced Structural Damage Detection, Segmentation, and Quantification Using Computer Vision and Deep Learning

dc.contributor.authorMeda, Dhathri
dc.contributor.authorAhmed, Mohammed Mustafa
dc.contributor.authorKalapatapu, Prafulla
dc.contributor.authorPasupuleti, Venkata Dilip Kumar
dc.date.accessioned2026-08-20T21:25:00Z
dc.date.available2026-08-20T21:25:00Z
dc.date.copyright2025/06/14
dc.date.issued2025
dc.identifier.otherJCCEE5.CPENG-6686.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4314416
dc.description.abstractAbstractIn infrastructure monitoring, manual inspections and conventional computer vision techniques have long been the standard for detecting structural damage. Nevertheless, these approaches are frequently constrained by their reliance on human ...
dc.publisherAmerican Society of Civil Engineers
dc.titleEnhanced Structural Damage Detection, Segmentation, and Quantification Using Computer Vision and Deep Learning
dc.typeJournal Article
dc.journal.volume39
dc.journal.issue5
dc.journal.titleJournal of Computing in Civil Engineering
dc.identifier.doi10.1061/JCCEE5.CPENG-6686
dc.journal.fristpage04025066-1
dc.journal.lastpage04025066-15
dc.page15
dc.treeJournal of Computing in Civil Engineering:;2025:;Volume ( 039 ):;issue: 005
dc.contenttypeFulltext


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