Intelligent Tunnel Crack Recognition Based on Automatic Sample Labeling

被引：0

作者：

Wang Y. ^{[1
,2
]}

Zhu L. ^{[1
,2
]}

Yu Z. ^{[1
,2
]}

Shi H. ^{[1
,2
]}

She C. ^{[3
]}

机构：

[1] Frontiers Science Center for Smart High-Speed Railway System, Beijing Jiaotong University, Beijing

[2] Ministry of Education, Key Laboratory of Vehicle Advanced Manufacturing, Measuring and Control Technology, Beijing Jiaotong University, Beijing

[3] Institute of Microelectronics, The Chinese Academy of Sciences, Beijing

来源：

Xinan Jiaotong Daxue Xuebao/Journal of Southwest Jiaotong University | 2023年 / 58卷 / 05期

关键词：

cracks; deep learning; image acquisition; image processing; sample labeling;

D O I：

10.3969/j.issn.0258-2724.20210092

中图分类号：

学科分类号：

摘要：

Detecting tunnel surface cracks has been one of the important tasks for subway operators. To achieve the automatic detection of tunnel cracks, this paper proposed an automatic labeling and recognition algorithm for tunnel crack samples, which combined crack feature extraction with deep learning. The paper also established an image feature library of crack samples based on the feature of tunnel cracks and improved the structure of the deep convolution network, namely AlexNet. In addition, the paper designed a track-sliding tunnel image acquisition system and inspection vehicle and then established a dataset consisting of 4 500 crack image samples and 1 500 test images, so as to verify the feasibility and effectiveness of the algorithm. The result shows that the clarity of the collected images meets the requirements, and the designed algorithm can complete the automatic labeling of cracks. The recognition rate of the crack image dataset is 97.8%, which can verify the effectiveness of the algorithm and the acquisition system. © 2023 Science Press. All rights reserved.

引用

页码：1001 / 1008and1036

共 12 条

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