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Vision-Language Artificial Intelligence for Robotic-Based Monitoring: Concrete Defect Detection, Classification, and Localization in Two-Dimensional Maps

dc.contributor.authorZade, Farzad Azizi
dc.contributor.authorEbrahimkhanlou, Arvin
dc.date.accessioned2026-08-20T21:26:41Z
dc.date.available2026-08-20T21:26:41Z
dc.date.copyright2025/11/27
dc.date.issued2026
dc.identifier.otherJCCEE5.CPENG-7001.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4314462
dc.description.abstractAbstractThis paper introduces a novel framework that combines vision-language models (VLMs) and localization techniques to detect, classify, and localize visual structural defects using moving platforms such as robots and handheld devices, with an ...
dc.publisherAmerican Society of Civil Engineers
dc.titleVision-Language Artificial Intelligence for Robotic-Based Monitoring: Concrete Defect Detection, Classification, and Localization in Two-Dimensional Maps
dc.typeJournal Article
dc.journal.volume40
dc.journal.issue2
dc.journal.titleJournal of Computing in Civil Engineering
dc.identifier.doi10.1061/JCCEE5.CPENG-7001
dc.journal.fristpage04025157-1
dc.journal.lastpage04025157-20
dc.page20
dc.treeJournal of Computing in Civil Engineering:;2026:;Volume ( 040 ):;issue: 002
dc.contenttypeFulltext


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