Res-FPN–SqueezeNet Segmentation Architecture for Enhanced Identification of Concrete Cracks within a Deep-Learning Framework

dc.contributor.author	Zhang, Chunzheng
dc.contributor.author	Mu, Tianwei
dc.contributor.author	Ning, Baokuan
dc.contributor.author	Wang, Xingkai
dc.contributor.author	Ma, Yuan
dc.date.accessioned	2026-08-20T21:24:31Z
dc.date.available	2026-08-20T21:24:31Z
dc.date.copyright	2025/06/05
dc.date.issued	2025
dc.identifier.other	JCCEE5.CPENG-6621.pdf
dc.identifier.uri	http://yetl.yabesh.ir/yetl1/handle/yetl/4314405
dc.description.abstract	AbstractTo enhance the precision and efficiency of concrete crack detection and segmentation in concrete structures, this paper proposes a novel Res-FPN–SqueezeNet segmentation model improved by the SqueezeNet convolutional neural network based on a deep ...
dc.publisher	American Society of Civil Engineers
dc.title	Res-FPN–SqueezeNet Segmentation Architecture for Enhanced Identification of Concrete Cracks within a Deep-Learning Framework
dc.type	Journal Article
dc.journal.volume	39
dc.journal.issue	5
dc.journal.title	Journal of Computing in Civil Engineering
dc.identifier.doi	10.1061/JCCEE5.CPENG-6621
dc.journal.fristpage	04025061-1
dc.journal.lastpage	04025061-13
dc.page	13
dc.tree	Journal of Computing in Civil Engineering:;2025:;Volume ( 039 ):;issue: 005
dc.contenttype	Fulltext