Deep learning based water leakage detection for shield tunnel lining  

作　　者：Shichang LIU Xu XU Gwanggil JEON Junxin CHEN Ben-Guo HE 

机构地区：[1]College of Computer Science,Sichuan University,Chengdu 610065,China [2]School of Computer Science and Engineering,Northeastern University,Shenyang 110004,China [3]Department of Embedded Systems Engineering,Incheon National University,Incheon 22012,Korea [4]School of Software,Dalian University of Technology,Dalian 116621,China [5]Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines,Northeastern University,Shenyang 110819,China

出　　处：《Frontiers of Structural and Civil Engineering》2024年第6期887-898,共12页结构与土木工程前沿（英文版）

基　　金：This work is funded by the National Natural Science Foundation of China(Grant Nos.62171114 and 52222810);the Fundamental Research Funds for the Central Universities(No.DUT22RC(3)099).

摘　　要：Shield tunnel lining is prone to water leakage,which may further bring about corrosion and structural damage to the walls,potentially leading to dangerous accidents.To avoid tedious and inefficient manual inspection,many projects use artificial intelligence(Al)to detect cracks and water leakage.A novel method for water leakage inspection in shield tunnel lining that utilizes deep learning is introduced in this paper.Our proposal includes a ConvNeXt-S backbone,deconvolutional-feature pyramid network(D-FPN),spatial attention module(SPAM).and a detection head.It can extract representative features of leaking areas to aid inspection processes.To further improve the model's robustness,we innovatively use an inversed low-light enhancement method to convert normally illuminated images to low light ones and introduce them into the training samples.Validation experiments are performed,achieving the average precision(AP)score of 56.8%,which outperforms previous work by a margin of 5.7%.Visualization illustrations also support our method's practical effectiveness.

关 键 词：water leakage detection deep learning deconvolutional-feature pyramid spatial attention 

分 类 号：U455.91[建筑科学—桥梁与隧道工程] TP391.41[交通运输工程—道路与铁道工程] TP18[自动化与计算机技术—计算机应用技术]