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Crack Segmentation on Underground Mine Tunnel based on Swin Transformer

Published: 17 April 2024 Publication History

Abstract

Regular inspection of surface cracks in underground mine tunnel is of paramount concern for the safe operation and maintenance of underground mine. Currently, crack detection predominantly rests on manual inspection methods, which are plagued by a host of challenges, including low detection accuracy, untimely assessments, high operational risks, substantial manpower requirements, and subjectivity. In contrast, machine vision techniques based on deep learning offer an effective solution to overcome the shortcomings of manual inspections. Therefore, this study introduces a crack segmentation network called SwinV2-Unet, which is built upon a pure Transformer architecture. It utilizes Swin Transformer V2 as the feature extraction layer, incorporating cross-window interactions and dynamic perception methods to comprehensively extract crack image features. Furthermore, this crack segmentation network combines the Encoder-Decoder structure of Unet to fuse crack features of different scales, enabling the network model to achieve more precise crack image segmentation. Finally, experimental validation is conducted on a proprietary dataset of underground mine tunnel cracks. When juxtaposed with outstanding algorithms such as Unet, U2net, DeeplabV3+, and Swin-Unet, our proposed SwinV2-Unet demonstrates superior performance in crack segmentation. The evaluation metrics, including Precision, Recall, F1-score, and Intersection over Union (IoU), yield values of 0.817, 0.847, 0.832, and 0.767, respectively.

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  • (2025)Deep learning for surface crack detection in civil engineering: A comprehensive reviewMeasurement10.1016/j.measurement.2025.116908248(116908)Online publication date: May-2025

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EITCE '23: Proceedings of the 2023 7th International Conference on Electronic Information Technology and Computer Engineering
October 2023
1809 pages
ISBN:9798400708305
DOI:10.1145/3650400
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 17 April 2024

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  • (2025)Deep learning for surface crack detection in civil engineering: A comprehensive reviewMeasurement10.1016/j.measurement.2025.116908248(116908)Online publication date: May-2025

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