Addressing Data Scarcity in GPR Road Defect Detection: A Novel Framework Combining Stable Diffusion and Efficient GCP-YOLO

dc.contributor.author	Cui, Minxing
dc.contributor.author	Du, Yanliang
dc.contributor.author	Wu, Difei
dc.contributor.author	Sun, Lijun
dc.contributor.author	Yan, Yu
dc.date.accessioned	2026-08-20T21:28:54Z
dc.date.available	2026-08-20T21:28:54Z
dc.date.copyright	2026/06/09
dc.date.issued	2026
dc.identifier.other	JCCEE5.CPENG-7693.pdf
dc.identifier.uri	http://yetl.yabesh.ir/yetl1/handle/yetl/4314526
dc.description.abstract	AbstractThe application of deep learning to ground penetrating radar (GPR) detection of urban road subsurface defects is often limited by the scarcity of real-world data and high computational costs. To address this, we propose a novel framework that ...
dc.publisher	American Society of Civil Engineers
dc.title	Addressing Data Scarcity in GPR Road Defect Detection: A Novel Framework Combining Stable Diffusion and Efficient GCP-YOLO
dc.type	Journal Article
dc.journal.volume	40
dc.journal.issue	5
dc.journal.title	Journal of Computing in Civil Engineering
dc.identifier.doi	10.1061/JCCEE5.CPENG-7693
dc.journal.fristpage	04026073-1
dc.journal.lastpage	04026073-17
dc.page	17
dc.tree	Journal of Computing in Civil Engineering:;2026:;Volume ( 040 ):;issue: 005
dc.contenttype	Fulltext