A Method of Identifying Surface Crack of Subway Tunnel in Consideration of Occlusions

HUANG Yuan-yuan; PENG Qian; XU Bing

doi:10.11988/ckyyb.20211276

PDF(6432 KB)

Journal of Changjiang River Scientific Research Institute ›› 2023, Vol. 40 ›› Issue (5) : 145-152. DOI: 10.11988/ckyyb.20211276

Engineering Safety and Disaster Prevention

A Method of Identifying Surface Crack of Subway Tunnel in Consideration of Occlusions

HUANG Yuan-yuan, PENG Qian, XU Bing

Author information +

History +

Abstract

To enhance the accuracy of identifying surface cracks in subway tunnels, we propose an algorithm for recognizing and extracting crack features in subway shield tunnels taking into account the presence of obstructions based on deep learning and machine vision technology. We employed the convolution neural network to extract the features of cracks using image restoration technology, thereby obtaining accurate information on the cracks. First, we proposed a U-net-based method of segmenting the occlusions on the subway tunnel segment surface, marking the position information by a mask map. Then, we built a Faster R-CNN network model using VGG-16 as the feature extraction network to locate and mark the cracks in the tunnel segment image. By combining the results of the two, we can determine the relationship between the cracks and occlusions. Finally, we proposed the deep learning convolution neural network model in which the occluded crack information can be restored. The trained network model can predict the cracks in the occluded area. The experimental results demonstrate that the accuracy rate of the proposed method reaches 90% in repairing the occluded crack information, indicating its application value in actual tunnel disaster detection and comprehensive evaluation.

Key words

shield tunnel / surface crack identification / feature extraction / deep learning / machine vision technology / image restoration

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

HUANG Yuan-yuan, PENG Qian, XU Bing. A Method of Identifying Surface Crack of Subway Tunnel in Consideration of Occlusions[J]. Journal of Changjiang River Scientific Research Institute. 2023, 40(5): 145-152 https://doi.org/10.11988/ckyyb.20211276

References

[1] 田时沫, 鲁放, 杨珂, 等. 2017年中国城市轨道交通运营线路统计与分析[J]. 都市快轨交通, 2018, 31(1): 16-20.
[2] 朱力强, 王春薇, 王耀东, 等. 基于特征点集距离描述的裂缝图像匹配算法研究[J]. 仪器仪表学报, 2016, 37(12): 2851-2858.
[3] ATTARD L, DEBONO C J, VALENTINO G,et al. Vision-Based Change Detection for Inspection of Tunnel Liners[J]. Automation in Construction, 2018, 91: 142 - 154.
[4] 周丽军,刘晓.基于分数阶傅里叶变换的隧道低对比度裂缝检测[J]. 计算机科学,2019,46(增刊1):208-210.
[5] 蒲炳荣, 漆泰岳, 黄晓东, 等. 隧道衬砌分岔裂缝特征提取研究[J]. 铁道标准设计, 2019, 63(10): 135-141.
[6] 张振海, 尹晓珍, 王阳萍, 等. 基于特征分析的图像式地铁隧道裂缝检测方法研究[J]. 铁道科学与工程学报, 2019, 16(11): 2791-2800.
[7] 王睿, 漆泰岳, 万宇, 等. 基于机器视觉的高铁隧道衬砌裂缝骨架拐点识别[J]. 地下空间与工程学报, 2020, 16(2): 524-530.
[8] ZHANG L, YANG F, ZHANG Y D, et al. Road Crack Detection Using Deep Convolutional Neural Network[C]//2016 IEEE International Conference on Image Processing (ICIP). September 25-28, 2016, Phoenix, AZ, USA. New York: IEEE Press, 2016: 3708-3712.
[9] CHA Y J, CHOI W,BÜYÜKÖZTÜRK O. Deep Learning-based Crack Damage Detection Using Convolutional Neural Networks[J]. Computer-aided Civil and Infrastructure Engineering, 2017, 32(5): 361-378.
[10] CHEN F C, JAHANSHAHI M R. NB-CNN: Deep Learning-Based Crack Detection Using Convolutional Neural Network and Nave Bayes Data Fusion[J]. IEEE Transactions on Industrial Electronics, 2018, 65(5): 4392-4400.
[11] 薛亚东, 李宜城. 基于深度学习的盾构隧道衬砌病害识别方法[J]. 湖南大学学报(自然科学版), 2018, 45(3): 100-109.
[12] 刘洪公, 王学军, 李冰莹, 等. 基于卷积神经网络的桥梁裂缝检测与识别[J]. 河北科技大学学报, 2016, 37(5): 485-490.
[13] 刘新根, 陈莹莹, 朱爱玺, 等. 基于深度学习的隧道裂缝识别方法[J]. 广西大学学报(自然科学版), 2018, 43(6): 2243-2251.
[14] 柴雪松, 朱兴永, 李健超, 等. 基于深度卷积神经网络的隧道衬砌裂缝识别算法[J]. 铁道建筑, 2018, 58(6): 60-65.
[15] 黄宏伟, 李庆桐. 基于深度学习的盾构隧道渗漏水病害图像识别[J]. 岩石力学与工程学报, 2017, 36(12): 2861-2871.
[16] 吴贺贺, 王安红, 王海东. 基于Faster R-CNN的隧道图像裂缝检测[J]. 太原科技大学学报, 2019, 40(3): 165-168.
[17] RONNEBERGER O,FISCHER P, BROX T. U-Net:Convolutional Networks for Biomedical Image Segmentation[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention. Munich, Germany. October 5-9, 2015: 234-241.
[18] SHELHAMER E, LONG J, DARRELL T. Fully Convolutional Networks for Semantic Segmentation[C]//IEEE Transactions on Pattern Analysis and Machine Intelligence. May 24, 2016, New York: IEEE Press, 2016: 640-651.
[19] 金飞, 王龙飞, 刘智, 等. 一种双U-net的遥感影像道路提取方法[J]. 测绘科学技术学报, 2019, 36(4): 377-381, 387.
[20] REN S, HE K, GIRSHICK R, et al. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137-1149.
[21] SIMONYAN K, ZISSERMAN A. Very Deep Convolutional Networks for Large-scale Image Recognition[EB/OL]. (2015-04-10)[2021-11-28]. https://arxiv.org/abs/1409.1556.
[22] PADILLA R, NETTO S L, DA SILVA E A B. A Survey on Performance Metrics for Object-Detection Algorithms[C]//2020 International Conference on Systems, Signals and Image Processing (IWSSIP). July 1-3, 2020, Niteroi, Brazil. New York: IEEE Press, 2020: 237-242.