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dc.contributor.authorRostami, Ghodsiyeh
dc.contributor.authorChen, Po-Han
dc.contributor.authorHosseini, Mahdi S.
dc.date.accessioned2026-08-20T21:27:23Z
dc.date.available2026-08-20T21:27:23Z
dc.date.copyright2026/02/06
dc.date.issued2026
dc.identifier.otherJCCEE5.CPENG-7090.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4314482
dc.description.abstractAbstractImage-based crack detection algorithms are increasingly in demand in infrastructure monitoring, as early detection of cracks is of paramount importance for timely maintenance planning. While deep learning has significantly advanced crack detection ...Practical ApplicationsInspecting infrastructure such as bridges, roads, and buildings is critical to ensure safety, however, traditional methods are often slow, expensive, and pose safety risks for workers. As a result, there has been a shift toward using ...
dc.publisherAmerican Society of Civil Engineers
dc.titleSegment Any Crack: Deep Semantic Segmentation Adaptation for Crack Detection
dc.typeJournal Article
dc.journal.volume40
dc.journal.issue3
dc.journal.titleJournal of Computing in Civil Engineering
dc.identifier.doi10.1061/JCCEE5.CPENG-7090
dc.journal.fristpage04026020-1
dc.journal.lastpage04026020-14
dc.page14
dc.treeJournal of Computing in Civil Engineering:;2026:;Volume ( 040 ):;issue: 003
dc.contenttypeFulltext


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