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Frontiers of Structural and Civil Engineering

ISSN 2095-2430

ISSN 2095-2449(Online)

CN 10-1023/X

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Front. Struct. Civ. Eng.    2024, Vol. 18 Issue (5) : 681-698    https://doi.org/10.1007/s11709-024-1048-4
Pixel-level crack segmentation of tunnel lining segments based on an encoder–decoder network
Shaokang HOU1,2,3, Zhigang OU1,3, Yuequn HUANG1,4, Yaoru LIU1()
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
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Abstract

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.
Keywords tunnel lining segment      crack detection      semantic segmentation      convolutional neural network      encoder–decoder structure     
Corresponding Author(s): Yaoru LIU   
Just Accepted Date: 24 May 2024   Online First Date: 18 June 2024    Issue Date: 26 June 2024
 Cite this article:   
Shaokang HOU,Zhigang OU,Yuequn HUANG, et al. Pixel-level crack segmentation of tunnel lining segments based on an encoder–decoder network[J]. Front. Struct. Civ. Eng., 2024, 18(5): 681-698.
 URL:  
https://academic.hep.com.cn/fsce/EN/10.1007/s11709-024-1048-4
https://academic.hep.com.cn/fsce/EN/Y2024/V18/I5/681
Fig.1  Overall structure of CrackRecNet.
No. Block/Layer name Convolution kernel size Convolution kernel number Stride Input channel Output channel Output size (pixels)
1 convolution block 3 × 3 2 1 3 64 448 × 448
2 max pooling layer 2 × 2 1 2 64 64 224 × 224
3 convolution block 3 × 3 2 1 64 128 224 × 224
4 max pooling layer 2 × 2 1 2 128 128 112 × 112
5 convolution block 3 × 3 4 1 128 256 112 × 112
6 max pooling layer 2 × 2 1 2 256 256 56 × 56
7 convolution block 3 × 3 4 1 256 512 56 × 56
8 max pooling layer 2 × 2 1 2 512 512 28 × 28
9 convolution block 3 × 3 4 1 512 512 28 × 28
10 max pooling layer 2 × 2 1 2 512 512 14 × 14
11 convolution block 3 × 3 1 1 512 512 14 × 14
12 transpose convolution layer 4 × 4 1 2 512 256 28 × 28
13 convolution layer 3 × 3 1 1 768 512 28 × 28
14 transpose convolution layer 4 × 4 1 2 512 256 56 × 56
15 convolution layer 3 × 3 1 1 768 512 56 × 56
16 transpose convolution layer 4 × 4 1 2 512 256 112 × 112
17 convolution layer 3 × 3 1 1 512 256 112 × 112
18 transpose convolution layer 4 × 4 1 2 256 64 224 × 224
19 convolution layer 3 × 3 1 1 192 128 224 × 224
20 transpose convolution layer 4 × 4 1 2 128 32 448 × 448
21 convolution layer 3 × 3 1 1 96 32 448 × 448
22 output layer 1 × 1 1 1 32 1 448 × 448
Tab.1  Detail parameters of CrackRecNet
Fig.2  Composition of each convolution block in the encoder.
Fig.3  Schematic diagram of copy and concatenate.
Fig.4  Photographs of lining segments at the project site: (a) uninstalled lining segments; (b) installed lining segments.
Fig.5  Cracking defects and noise interferences in tunnel lining segments: (a) high light condition; (b) low light condition.
Fig.6  Image acquisition equipment and image acquisition photograph at the tunnel project site.
Fig.7  Data preprocessing process of lining segment crack images.
Fig.8  Example of data preprocessing for a lining segment crack image.
Fig.9  Tunnel lining crack image annotation process.
Fig.10  Several examples of the image annotation.
Computing environment Configuration/version
Operating system Windows 10 Pro for Wrokstations
CPU Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz
GPU NVIDIA TITAN V
CUDA NVIDIA CUDA 11.2.109 driver
cuDNN NVIDIA cuDNN 11.2
DL framework Pytorch 1.8.1
Programming environment Python 3.7 and several Pytroch packages
Tab.2  Computing environment of the CrackRecNet in the experiment
Hyperparameter Value Remarks
Learning rate 0.1 (initial value) attenuation coefficient is 0.1
Momentum 0.9 –
Weight decay 10−4 L2 regularization
Batch size 16 4 per GPU
Epochs 90 –
Worker thread 4 –
Tab.3  Hyperparameter statistics of the proposed CrackRecNet model
Actual class Predicted class
Positive Negative
Positive true positive (TP) false negative (FN)
Negative false positive (FP) true negative (TN)
Tab.4  Basic confusion matrix
Fig.11  Variation of loss with training epochs.
Fig.12  Semantic segmentation results of several images with cracks in the test set.
Fig.13  Semantic segmentation results of several images without cracks in the test set.
Fig.14  Several output examples with poor crack segmentation effect.
Processor configuration Processing time (seconds per image) Speedup ratio
CPU 1.55 1.00
Single-GPU 0.226 5.85
Multi-GPU 0.100 14.50
Tab.5  Training time cost under different processor configurations
Model Recall (%) Precision (%) F1 (%) PA (%) IoU (%) Dice (%)
U-Net 65.51 75.04 69.95 98.60 51.06 62.58
TernausNet 68.55 77.28 72.65 98.68 56.71 68.51
ResU-Net 67.11 79.02 72.58 98.67 56.45 68.09
CrackRecNet 65.21 82.94 73.01 98.66 58.21 69.85
Tab.6  Evaluation metrics statistics of four established models
Fig.15  Comparison of several test samples using U-Net, TernausNet, and CrackRecNet: (a) example 1; (b) example 2; (c) example 3; (d) example 4; (e) example 5; (f) example 6; (g) example 7.
Fig.16  Schematic diagram of the crack skeleton.
Fig.17  An example of the crack skeleton extraction process of a lining segment crack image.
Fig.18  Schematic diagram of the calculation principle of maximum actual width of crack.
Fig.19  Calculated width value, actual width value and absolute relative error of 15 lining segment crack instances.
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