基于深度学习的盾构隧道表观病害自动检测方法

doi:10.16183/j.cnki.jsjtu.2023.089

上海交通大学学报 ›› 2024, Vol. 58 ›› Issue (11): 1716-1723.doi: 10.16183/j.cnki.jsjtu.2023.089

基于深度学习的盾构隧道表观病害自动检测方法

王宝坤¹, 王如路², 陈锦剑¹(), 潘越¹, 王鲁杰²

1.上海交通大学土木工程系,上海 200240
2.上海申通地铁集团有限公司,上海 200070

收稿日期:2023-03-14 修回日期:2023-05-22 接受日期:2023-05-29 出版日期:2024-11-28 发布日期:2024-12-02
通讯作者: 陈锦剑,教授,博士生导师;E-mail:chenjj29@sjtu.edu.cn.
作者简介:王宝坤(1998—),硕士生,从事隧道病害图像处理与算法研究.
基金资助:
国家自然科学基金(72201171);上海市优秀学术带头人计划(20XD1422100)

Automatic Detection Method for Surface Diseases of Shield Tunnel Based on Deep Learning

WANG Baokun¹, WANG Rulu², CHEN Jinjian¹(), PAN Yue¹, WANG Lujie²

1. Department of Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
2. Shanghai Shentong Metro Group Co., Ltd., Shanghai 200070, China

Received:2023-03-14 Revised:2023-05-22 Accepted:2023-05-29 Online:2024-11-28 Published:2024-12-02

摘要/Abstract

摘要：

为实现高精度像素级地铁盾构隧道表观多病害检测,提出一种基于深度学习的语义分割模型 SU-ResNet++.首先,设计基于残差单元结合注意力机制的编码器 SE-ResNet50 进行预训练,并将其作为 U-Net++ 的主干网络设计新型神经网络模型;其次,通过原始数据采集、数据预处理及人工标注,构建具有 4 500 张图片的盾构隧道表观多病害数据集;最后,将所提出的方法通过数据集进行训练、验证和测试,并应用于实际工程检测,实现了高精度像素级的病害语义分割.实验结果表明,所提出的 SU-ResNet++ 算法适用于盾构隧道病害数据检测,可以自动准确地识别病害类别及形态,病害识别精度相比传统语义分割模型有明显提高,并且满足实际工程需求.

关键词: 地铁盾构隧道, 数据集构建, 语义分割, 深度迁移学习, U-Net++网络

Abstract:

In order to achieve high-precision pixel-level detection of multiple surface diseases in metro shield tunnels, a semantic segmentation model SU-ResNet++ based on deep learning is proposed. First, the encoder SE-ResNet50 based on residual unit and attention mechanism is designed and pre-trained, using as the backbone network of U-Net++ to design a new neural network model. Then, through original data collection, data preprocessing, and manual annotation, a shield tunnel surface multiple diseases dataset with 4 500 pictures is constructed. Finally, the proposed method is trained, verified, and tested on a dataset, and applied to practical engineering detection, achieving high-precision pixel-level diseases semantic segmentation. The experimental results indicate that the proposed SU-ResNet++ algorithm is applicable to the detection of shield tunnel disease data, and can automatically and accurately identify the disease category and form. Compared with the traditional semantic segmentation models, its disease identification precision is significantly improved, which meets the practical engineering requirements.

Key words: metro shield tunnel, dataset construction, semantic segmentation, deep transfer learning, U-Net++ networks

中图分类号:

王宝坤, 王如路, 陈锦剑, 潘越, 王鲁杰. 基于深度学习的盾构隧道表观病害自动检测方法[J]. 上海交通大学学报, 2024, 58(11): 1716-1723.

WANG Baokun, WANG Rulu, CHEN Jinjian, PAN Yue, WANG Lujie. Automatic Detection Method for Surface Diseases of Shield Tunnel Based on Deep Learning[J]. Journal of Shanghai Jiao Tong University, 2024, 58(11): 1716-1723.

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模型	P/%	R/%	I/%
U-Net++	92.26	81.92	80.86
ResNet50	88.38	78.64	72.19
SU-ResNet++	95.72	85.86	81.34

模型	P/%	R/%	I/%
U-Net	90.12	79.86	72.98
VGG19	87.72	78.88	70.05
AlexNet	85.59	75.50	66.63
SU-ResNet++	95.72	85.86	81.34

基于深度学习的盾构隧道表观病害自动检测方法

Automatic Detection Method for Surface Diseases of Shield Tunnel Based on Deep Learning

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