A Multi-Information Fusion Method for Repetitive Tunnel Disease Detection

Zhiyuan Gan, Li Teng, Ying Chang, Xinyang Feng, Mengnan Gao and Xinwen Gao ()
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Zhiyuan Gan: School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China
Li Teng: Shanghai Urban Construction City Operation (Group) Co., Ltd., Shanghai 200023, China
Ying Chang: Shanghai Urban Construction City Operation (Group) Co., Ltd., Shanghai 200023, China
Xinyang Feng: School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China
Mengnan Gao: School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China
Xinwen Gao: School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China

Sustainability, 2024, vol. 16, issue 10, 1-24

Abstract: Existing tunnel defect detection methods often lack repeated inspections, limiting longitudinal analysis of defects. To address this, we propose a multi-information fusion approach for continuous defect monitoring. Initially, we utilized the You Only Look Once version 7 (Yolov7) network to identify defects in tunnel lining videos. Subsequently, defect localization is achieved with Super Visual Odometer (SuperVO) algorithm. Lastly, the SuperPoint–SuperGlue Matching Network (SpSg Network) is employed to analyze similarities among defect images. Combining the above information, the repeatability detection of the disease is realized. SuperVO was tested in tunnels of 159 m and 260 m, showcasing enhanced localization accuracy compared to traditional visual odometry methods, with errors measuring below 0.3 m on average and 0.8 m at maximum. The SpSg Network surpassed the depth-feature-based Siamese Network in image matching, achieving a precision of 96.61%, recall of 93.44%, and F1 score of 95%. These findings validate the effectiveness of this approach in the repetitive detection and monitoring of tunnel defects.

Keywords: disease detection; water leakage; visual odometry; image matching; deep learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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