SD-YOLO: An Efficient Deep Learning Framework for Real-Time and Multiscale Pavement Crack Detection

dc.contributor.author	Zhang, Huan
dc.contributor.author	Liu, Pengfei
dc.contributor.author	Zhang, Xiaoguo
dc.contributor.author	Yang, Yuan
dc.contributor.author	Liu, Youchen
dc.contributor.author	Wang, Qing
dc.date.accessioned	2026-08-20T11:12:19Z
dc.date.available	2026-08-20T11:12:19Z
dc.date.copyright	2026/06/03
dc.date.issued	2026
dc.identifier.other	JITSE4.ISENG-2864.pdf
dc.identifier.uri	http://yetl.yabesh.ir/yetl1/handle/yetl/4311841
dc.description.abstract	AbstractPavement crack detection is crucial for maintaining urban infrastructure and ensuring traffic safety. However, existing methods often struggle with issues such as poor accuracy in complex environments, inadequate handling of diverse crack ...
dc.publisher	American Society of Civil Engineers
dc.title	SD-YOLO: An Efficient Deep Learning Framework for Real-Time and Multiscale Pavement Crack Detection
dc.type	Journal Article
dc.journal.volume	32
dc.journal.issue	3
dc.journal.title	Journal of Infrastructure Systems
dc.identifier.doi	10.1061/JITSE4.ISENG-2864
dc.journal.fristpage	04026013-1
dc.journal.lastpage	04026013-16
dc.page	16
dc.tree	Journal of Infrastructure Systems:;2026:;Volume ( 032 ):;issue: 003
dc.contenttype	Fulltext