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JCNet-Based Multidistress and Surface Feature Detection in Pavement Images at Pixel Level

dc.contributor.authorCheng, Huixuan
dc.contributor.authorZhang, Allen A.
dc.contributor.authorWang, Dingfeng
dc.contributor.authorShang, Jing
dc.contributor.authorHe, Anzheng
dc.contributor.authorPeng, Yi
dc.contributor.authorWei, Yifan
dc.contributor.authorZhan, You
dc.date.accessioned2026-08-20T21:27:52Z
dc.date.available2026-08-20T21:27:52Z
dc.date.copyright2026/01/29
dc.date.issued2026
dc.identifier.otherJCCEE5.CPENG-7183.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4314496
dc.description.abstractAbstractAutomated road maintenance depends on the accurate recognition of pavement distresses. Traditional manual inspection methods have difficulty meeting the high-frequency and high-precision detection for automated pavement maintenance. This paper ...
dc.publisherAmerican Society of Civil Engineers
dc.titleJCNet-Based Multidistress and Surface Feature Detection in Pavement Images at Pixel Level
dc.typeJournal Article
dc.journal.volume40
dc.journal.issue3
dc.journal.titleJournal of Computing in Civil Engineering
dc.identifier.doi10.1061/JCCEE5.CPENG-7183
dc.journal.fristpage04026011-1
dc.journal.lastpage04026011-15
dc.page15
dc.treeJournal of Computing in Civil Engineering:;2026:;Volume ( 040 ):;issue: 003
dc.contenttypeFulltext


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