Automatic Recognition of Tunnel Water Leakage Based on Adaptive Information Extraction Network and Multiscale Feature Enhancement Module
Water leakage in metro tunnels is a critical safety indicator, necessitating regular inspections to avert catastrophic failures. Deep learning-based computer vision is currently utilized to detect water leakage in metro tunnels. However, challenges like large model parameters, low detection accuracy...
Saved in:
| Main Authors: | Dandan Wang, Gongyu Hou, Qinhuang Chen, Weiyi Li, Haoxiang Li, Yaohua Shao, Xunan Yu |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10804770/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
SE-TransUNet-Based Semantic Segmentation for Water Leakage Detection in Tunnel Secondary Linings Amid Complex Visual Backgrounds
by: Renjie Song, et al.
Published: (2025-07-01) -
Multiple Water and Sand Leakage Model Tests for Shield Tunnels
by: Emmet Amonee Greene, et al.
Published: (2024-11-01) -
Mechanical Response of Pipeline Leakage to Existing Tunnel Structures: Insights from Numerical Modeling
by: Ruichuan Zhao, et al.
Published: (2025-05-01) -
Automatic Detection Method for Surface Diseases of Shield Tunnel Based on Deep Learning
by: WANG Baokun, WANG Rulu, CHEN Jinjian, PAN Yue, WANG Lujie
Published: (2024-11-01) -
Hyperclustering: High-Order Deep/Shallow Feature Clustering for Subway Shield Tunneling Water Leakage Detection
by: Jianjun Xu, et al.
Published: (2025-01-01)