Pixel-level crack segmentation of tunnel lining segments based on an encoder–decoder network  

作　　者：Shaokang HOU Zhigang OU Yuequn HUANG Yaoru LIU 

机构地区：[1]State Key Laboratory of Hydroscience and Engineering,Tsinghua University,Beijing 100084,China [2]China Renewable Energy Engineering Institute,Beijing 100120,China [3]State Key Laboratory of Stimulation and Regulation of Water Cycles in River Basins,China Institute of Water Resources and Hydropower Research,Beijing 100038,China [4]Hunan Provincial Water Resources Development&Investment Co.,Ltd.,Changsha 410007,China

出　　处：《Frontiers of Structural and Civil Engineering》2024年第5期681-698,共18页结构与土木工程前沿（英文版）

基　　金：This work was supported by the National Natural Science Foundation of China(Grant Nos.52179105 and 41941019);Science and Technology Innovation Project of Quanmutang Engineering.

摘　　要：Regular detection and repair for lining cracks are necessary to guarantee the safety and stability of tunnels.The development of computer vision has greatly promoted structural health monitoring.This study proposes a novel encoder–decoder structure,CrackRecNet,for semantic segmentation of lining segment cracks by integrating improved VGG-19 into the U-Net architecture.An image acquisition equipment is designed based on a camera,3-dimensional printing(3DP)bracket and two laser rangefinders.A tunnel concrete structure crack(TCSC)image data set,containing images collected from a double-shield tunnel boring machines(TBM)tunnel in China,was established.Through data preprocessing operations,such as brightness adjustment,pixel resolution adjustment,flipping,splitting and annotation,2880 image samples with pixel resolution of 448×448 were prepared.The model was implemented by Pytorch in PyCharm processed with 4 NVIDIA TITAN V GPUs.In the experiments,the proposed CrackRecNet showed better prediction performance than U-Net,TernausNet,and ResU-Net.This paper also discusses GPU parallel acceleration effect and the crack maximum width quantification.

关 键 词：tunnel lining segment crack detection semantic segmentation convolutional neural network encoder-decoder structure 

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]