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Optimized Pavement Crack Segmentation with Low Computational Cost Using Fusion-Enhanced Attention U-Net

dc.contributor.authorMohammed, Mohammed Ameen
dc.contributor.authorHan, Zheng
dc.contributor.authorLi, Yange
dc.contributor.authorAl-Huda, Zaid
dc.contributor.authorWang, Weidong
dc.date.accessioned2026-08-20T21:24:11Z
dc.date.available2026-08-20T21:24:11Z
dc.date.copyright2026/04/29
dc.date.issued2026
dc.identifier.otherJCCEE5.CPENG-6525.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4314394
dc.description.abstractAbstractAccurate segmentation of pavement cracks is crucial for maintaining road safety and the longevity of road infrastructures. Existing convolutional neural network (CNN) models often struggle with the noise and irregular crack patterns inherent in ...
dc.publisherAmerican Society of Civil Engineers
dc.titleOptimized Pavement Crack Segmentation with Low Computational Cost Using Fusion-Enhanced Attention U-Net
dc.typeJournal Article
dc.journal.volume40
dc.journal.issue4
dc.journal.titleJournal of Computing in Civil Engineering
dc.identifier.doi10.1061/JCCEE5.CPENG-6525
dc.journal.fristpage04026041-1
dc.journal.lastpage04026041-14
dc.page14
dc.treeJournal of Computing in Civil Engineering:;2026:;Volume ( 040 ):;issue: 004
dc.contenttypeFulltext


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