Image-based intelligent detection of typical defects of complex subway tunnel surface
We propose a new hybrid model algorithm that addresses the challenges of recognizing multiple objects. Specifically, objects are recognized from massive, large-scale, and complex tunnel images without repeated parameter adjustments and high-cost annotation datasets. Our algorithm utilizes a multi-sc...
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| Veröffentlicht in: | Tunnelling and underground space technology Jg. 140; S. 105266 |
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| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Elsevier Ltd
01.10.2023
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| ISSN: | 0886-7798 |
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| Abstract | We propose a new hybrid model algorithm that addresses the challenges of recognizing multiple objects. Specifically, objects are recognized from massive, large-scale, and complex tunnel images without repeated parameter adjustments and high-cost annotation datasets. Our algorithm utilizes a multi-scale, fusion-based encoder–decoder segmentation model to classify objects from high-resolution images of the tunnel surfaces. To enhance the accuracy of crack identification from complex backgrounds, we incorporate the Expanded Threshold Search (ETS) algorithm and the Local Window Extraction (LWE) algorithm. The acquisition device and the algorithm, implementing the multi-object dataset, have successfully tested, whereby it recognizes five objects and attains the highest Intersection over Union (39.3% for the crack object, 65.6% for the leakage object, and 75.7% for the rest).
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•An image acquisition device is designed for tunnel.•A multi-object dataset of complex subway tunnel images is efficiently built.•An efficient and accurate hybrid model is proposed.•Comprehensive experimental results are provided. |
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| AbstractList | We propose a new hybrid model algorithm that addresses the challenges of recognizing multiple objects. Specifically, objects are recognized from massive, large-scale, and complex tunnel images without repeated parameter adjustments and high-cost annotation datasets. Our algorithm utilizes a multi-scale, fusion-based encoder–decoder segmentation model to classify objects from high-resolution images of the tunnel surfaces. To enhance the accuracy of crack identification from complex backgrounds, we incorporate the Expanded Threshold Search (ETS) algorithm and the Local Window Extraction (LWE) algorithm. The acquisition device and the algorithm, implementing the multi-object dataset, have successfully tested, whereby it recognizes five objects and attains the highest Intersection over Union (39.3% for the crack object, 65.6% for the leakage object, and 75.7% for the rest).
[Display omitted]
•An image acquisition device is designed for tunnel.•A multi-object dataset of complex subway tunnel images is efficiently built.•An efficient and accurate hybrid model is proposed.•Comprehensive experimental results are provided. |
| ArticleNumber | 105266 |
| Author | Xu, Lizhi Wang, Yaodong Shi, Hongmei Yu, Zujun Zhu, Liqiang Dong, Anqi |
| Author_xml | – sequence: 1 givenname: Lizhi orcidid: 0000-0003-3770-4522 surname: Xu fullname: Xu, Lizhi email: 22110419@bjtu.edu.cn organization: School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing, 100044, China – sequence: 2 givenname: Yaodong surname: Wang fullname: Wang, Yaodong email: ydwang@bjtu.edu.cn organization: School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing, 100044, China – sequence: 3 givenname: Anqi orcidid: 0000-0002-0365-0733 surname: Dong fullname: Dong, Anqi email: anqid2@uci.edu organization: Department of Mechanical and Aerospace Engineering, University of California, Irvine, CA, USA – sequence: 4 givenname: Liqiang surname: Zhu fullname: Zhu, Liqiang email: lqzhu@bjtu.edu.cn organization: School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing, 100044, China – sequence: 5 givenname: Hongmei surname: Shi fullname: Shi, Hongmei email: hmshi@bjtu.edu.cn organization: School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing, 100044, China – sequence: 6 givenname: Zujun surname: Yu fullname: Yu, Zujun email: zjyu@bjtu.edu.cn organization: School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing, 100044, China |
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| Keywords | Intelligent detection Structural health monitoring Semantic segmentation Subway tunnel Image processing algorithm Convolutional neural network |
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