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Automated Tracking of Worker and Heavy Equipment on Tunnel Construction Sites: Deep-Learning Framework

dc.contributor.authorLi, Linchao
dc.contributor.authorCui, Xiaodong
dc.contributor.authorWang, Junzheng
dc.contributor.authorJin, Hao
dc.contributor.authorXu, Hongbin
dc.date.accessioned2026-08-20T10:38:44Z
dc.date.available2026-08-20T10:38:44Z
dc.date.copyright2025/11/17
dc.date.issued2026
dc.identifier.otherJCEMD4.COENG-16807.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4311067
dc.description.abstractAbstractTunnel construction environments pose significant challenges for real-time monitoring due to narrow spaces, poor lighting, and high densities of personnel and machinery. This paper proposes an enhanced deep-learning framework for automated ...
dc.publisherAmerican Society of Civil Engineers
dc.titleAutomated Tracking of Worker and Heavy Equipment on Tunnel Construction Sites: Deep-Learning Framework
dc.typeJournal Article
dc.journal.volume152
dc.journal.issue2
dc.journal.titleJournal of Construction Engineering and Management
dc.identifier.doi10.1061/JCEMD4.COENG-16807
dc.journal.fristpage04025237-1
dc.journal.lastpage04025237-16
dc.page16
dc.treeJournal of Construction Engineering and Management:;2026:;Volume ( 152 ):;issue: 002
dc.contenttypeFulltext


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