An integrated underwater structural multi-defects automatic identification and quantification framework for hydraulic tunnel via machine vision and deep learning

Autor(en):	Yangtao Li (State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing, China) Tengfei Bao (State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing, China) Xianjun Huang (State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing, China) Ruijie Wang (State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing, China) Xiaosong Shu (State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing, China) Bo Xu (College of Hydraulic Science and Engineering, Yangzhou University, Yangzhou, China) Jiuzhou Tu (State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing, China) Yuhang Zhou (State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing, China) Kang Zhang (State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing, China)
Medium:	Fachartikel
Sprache(n):	Englisch
Veröffentlicht in:	Structural Health Monitoring, Dezember 2022, n. 4, v. 22
Seite(n):	147592172211223
DOI:	10.1177/14759217221122316

Structurae kann Ihnen derzeit diese Veröffentlichung nicht im Volltext zur Verfügung stellen. Der Volltext ist beim Verlag erhältlich über die DOI: 10.1177/14759217221122316.

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