An integrated underwater structural multi-defects automatic identification and quantification framework for hydraulic tunnel via machine vision and deep learning
| Author(s): |
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: | journal article |
| Language(s): | English |
| Published in: | Structural Health Monitoring, December 2022, n. 4, v. 22 |
| Page(s): | 147592172211223 |
| DOI: | 10.1177/14759217221122316 |
- About this
data sheet - Reference-ID
10702213 - Published on:
16/12/2022 - Last updated on:
21/06/2023
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