WLR-Net: An Improved YOLO-V7 With Edge Constraints and Attention Mechanism for Water Leakage Recognition in the Tunnel-Reference-Cited by-同舟云学术

WLR-Net: An Improved YOLO-V7 With Edge Constraints and Attention Mechanism for Water Leakage Recognition in the Tunnel

Published:2024-08 Issue:4 Volume:8 Page:3105-3116
ISSN:2471-285X
Container-title:IEEE Transactions on Emerging Topics in Computational Intelligence
language:
Short-container-title:IEEE Trans. Emerg. Top. Comput. Intell.

Author:

Chen Junxin¹^ORCID,Xu Xu²^ORCID,Jeon Gwanggil³^ORCID,Camacho David⁴^ORCID,He Ben-Guo⁵^ORCID

Affiliation:

1. School of Software, Dalian University of Technology, Dalian, China

2. School of Computer Science and Engineering, Northeastern University, Shenyang, China

3. College of Information Technology, Incheon National University, Incheon, South Korea

4. School of Computer Systems Engineering, Universidad Politécnica de Madrid, Madrid, Spain

5. Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, Northeastern University, Shenyang, China

Funder

HISTERA IV Cofund 2021 Program through Project

European Union NextGenerationEU/PRTR”

Spanish Ministry of Science and Innovation under FightDIS

XAI-Disinfodemics

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Xiaomi Young Talents Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/7433297/10607834/10471596.pdf?arnumber=10471596

Reference49 articles.

1. Probabilistic analysis of crown settlement in high-speed railway tunnel constructed by sequential excavation method considering soil spatial variability

2. Case study on repair work for excessively deformed shield tunnel under accidental surface surcharge in soft clay;Shao;Chin. J. Geotechnical Eng.,2016

3. Acquiring sectional profile of metro tunnels using charge-coupled device cameras

4. Inspection equipment study for subway tunnel defects by grey-scale image processing

5. Deep Convolutional Neural Networks for efficient vision based tunnel inspection

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