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dc.contributor.authorZhang, Huan
dc.contributor.authorFeng, Jinyan
dc.contributor.authorDai, Chenghao
dc.contributor.authorTong, Zhaoxia
dc.date.accessioned2026-08-20T21:24:54Z
dc.date.available2026-08-20T21:24:54Z
dc.date.copyright2025/08/20
dc.date.issued2025
dc.identifier.otherJCCEE5.CPENG-6675.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4314414
dc.description.abstractAbstractThis study proposes a novel pavement crack segmentation methodology that integrates synthetic data sets with unsupervised domain adaptation to address annotation challenges in pavement crack data sets and enhance segmentation performance. An ...Practical ApplicationsAutomated crack detection plays a crucial role in structural health monitoring by enhancing speed, precision, and reliability. However, training deep learning models necessitates a vast amount of labeled real-world data, posing ...
dc.publisherAmerican Society of Civil Engineers
dc.titlePavement Crack Segmentation Based on Synthetic Data Sets and Unsupervised Domain Adaptation
dc.typeJournal Article
dc.journal.volume39
dc.journal.issue6
dc.journal.titleJournal of Computing in Civil Engineering
dc.identifier.doi10.1061/JCCEE5.CPENG-6675
dc.journal.fristpage04025100-1
dc.journal.lastpage04025100-17
dc.page17
dc.treeJournal of Computing in Civil Engineering:;2025:;Volume ( 039 ):;issue: 006
dc.contenttypeFulltext


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