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dc.contributor.authorZhao, Quanman
dc.contributor.authorLiu, Zhaohui
dc.contributor.authorMeng, Fanyu
dc.contributor.authorMa, Zhihao
dc.contributor.authorTuerhong, Aihemaitijiang
dc.contributor.authorGuo, Guihong
dc.date.accessioned2026-08-20T12:01:00Z
dc.date.available2026-08-20T12:01:00Z
dc.date.copyright2025/06/16
dc.date.issued2025
dc.identifier.otherJPEODX.PVENG-1635.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4312976
dc.description.abstractAbstractTo address challenges in the accuracy and integrity of semantic segmentation models for highway asphalt pavement crack detection, a novel model, highway crack net (HWCNet), is proposed, combining convolutional neural network (CNN) and transformer ...
dc.publisherAmerican Society of Civil Engineers
dc.titleHWCNet: A Semantic Segmentation Model for Highway Asphalt Pavement Cracks
dc.typeJournal Article
dc.journal.volume151
dc.journal.issue3
dc.journal.titleJournal of Transportation Engineering, Part B: Pavements
dc.identifier.doi10.1061/JPEODX.PVENG-1635
dc.journal.fristpage04025035-1
dc.journal.lastpage04025035-15
dc.page15
dc.treeJournal of Transportation Engineering, Part B: Pavements:;2025:;Volume ( 151 ):;issue: 003
dc.contenttypeFulltext


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