CGV-Net: Tunnel Lining Crack Segmentation Method Based on Graph Convolution Guided Transformer
Lining cracking is among the most prevalent forms of tunnel distress, posing significant threats to tunnel operations and vehicular safety. The segmentation of tunnel lining cracks is often hindered by the influence of complex environmental factors, which makes relying solely on local feature extrac...
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MDPI AG
2025-01-01
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| Series: | Buildings |
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| Online Access: | https://www.mdpi.com/2075-5309/15/2/197 |
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| author | Kai Liu Tao Ren Zhangli Lan Yang Yang Rong Liu Yuantong Xu |
| author_facet | Kai Liu Tao Ren Zhangli Lan Yang Yang Rong Liu Yuantong Xu |
| author_sort | Kai Liu |
| collection | DOAJ |
| description | Lining cracking is among the most prevalent forms of tunnel distress, posing significant threats to tunnel operations and vehicular safety. The segmentation of tunnel lining cracks is often hindered by the influence of complex environmental factors, which makes relying solely on local feature extraction insufficient for achieving high segmentation accuracy. To address this issue, this study proposes CGV-Net (CNN, GNN, and ViT networks), a novel tunnel crack segmentation network model that integrates convolutional neural networks (CNNs), graph neural networks (GNNs), and Vision Transformers (ViTs). By fostering information exchange among local features, the model enhances comprehension of the global structural patterns of cracks and improves inference capabilities in recognizing intricate crack configurations. This approach effectively addresses the challenge of modeling contextual information in crack feature extraction. Additionally, the Detailed-Macro Feature Fusion (DMFF) module enables multi-scale feature integration by combining detailed and coarse-grained features, mitigating the significant feature loss encountered during the encoding and decoding stages, and further improving segmentation precision. To overcome the limitations of existing public datasets, which often feature a narrow range of crack types and simplistic backgrounds, this study introduces TunnelCrackDB, a dataset encompassing diverse crack types and complex backgrounds.Experimental evaluations on both the public Crack dataset and the newly developed TunnelCrackDB demonstrate the efficacy of CGV-Net. On the Crack dataset, CGV-Net achieves accuracy, recall, and F1 scores of 73.27% and 57.32%, respectively. On TunnelCrackDB, CGV-Net attains accuracy, recall, and F1 scores of 81.15%, 83.54%, and 82.33%, respectively, showcasing its superior performance in challenging segmentation tasks. |
| format | Article |
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| institution | Kabale University |
| issn | 2075-5309 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Buildings |
| spelling | doaj-art-e4c2db5922484f0185cb3d1fe3ee1ab62025-01-24T13:26:08ZengMDPI AGBuildings2075-53092025-01-0115219710.3390/buildings15020197CGV-Net: Tunnel Lining Crack Segmentation Method Based on Graph Convolution Guided TransformerKai Liu0Tao Ren1Zhangli Lan2Yang Yang3Rong Liu4Yuantong Xu5School of Information Science and Engineering, Chongqing Jiaotong University, Chongqing 400074, ChinaSchool of Information Science and Engineering, Chongqing Jiaotong University, Chongqing 400074, ChinaSchool of Information Science and Engineering, Chongqing Jiaotong University, Chongqing 400074, ChinaChina Railway Changjiang Transport Design Group Co., Ltd., Chongqing 400067, ChinaInstitute of Future Civil Engineering Science and Technology, Chongqing Jiaotong University, Chongqing 400074, ChinaSchool of Information Science and Engineering, Chongqing Jiaotong University, Chongqing 400074, ChinaLining cracking is among the most prevalent forms of tunnel distress, posing significant threats to tunnel operations and vehicular safety. The segmentation of tunnel lining cracks is often hindered by the influence of complex environmental factors, which makes relying solely on local feature extraction insufficient for achieving high segmentation accuracy. To address this issue, this study proposes CGV-Net (CNN, GNN, and ViT networks), a novel tunnel crack segmentation network model that integrates convolutional neural networks (CNNs), graph neural networks (GNNs), and Vision Transformers (ViTs). By fostering information exchange among local features, the model enhances comprehension of the global structural patterns of cracks and improves inference capabilities in recognizing intricate crack configurations. This approach effectively addresses the challenge of modeling contextual information in crack feature extraction. Additionally, the Detailed-Macro Feature Fusion (DMFF) module enables multi-scale feature integration by combining detailed and coarse-grained features, mitigating the significant feature loss encountered during the encoding and decoding stages, and further improving segmentation precision. To overcome the limitations of existing public datasets, which often feature a narrow range of crack types and simplistic backgrounds, this study introduces TunnelCrackDB, a dataset encompassing diverse crack types and complex backgrounds.Experimental evaluations on both the public Crack dataset and the newly developed TunnelCrackDB demonstrate the efficacy of CGV-Net. On the Crack dataset, CGV-Net achieves accuracy, recall, and F1 scores of 73.27% and 57.32%, respectively. On TunnelCrackDB, CGV-Net attains accuracy, recall, and F1 scores of 81.15%, 83.54%, and 82.33%, respectively, showcasing its superior performance in challenging segmentation tasks.https://www.mdpi.com/2075-5309/15/2/197tunnel crack segmentationDMFFvision transformerCGV-Net |
| spellingShingle | Kai Liu Tao Ren Zhangli Lan Yang Yang Rong Liu Yuantong Xu CGV-Net: Tunnel Lining Crack Segmentation Method Based on Graph Convolution Guided Transformer Buildings tunnel crack segmentation DMFF vision transformer CGV-Net |
| title | CGV-Net: Tunnel Lining Crack Segmentation Method Based on Graph Convolution Guided Transformer |
| title_full | CGV-Net: Tunnel Lining Crack Segmentation Method Based on Graph Convolution Guided Transformer |
| title_fullStr | CGV-Net: Tunnel Lining Crack Segmentation Method Based on Graph Convolution Guided Transformer |
| title_full_unstemmed | CGV-Net: Tunnel Lining Crack Segmentation Method Based on Graph Convolution Guided Transformer |
| title_short | CGV-Net: Tunnel Lining Crack Segmentation Method Based on Graph Convolution Guided Transformer |
| title_sort | cgv net tunnel lining crack segmentation method based on graph convolution guided transformer |
| topic | tunnel crack segmentation DMFF vision transformer CGV-Net |
| url | https://www.mdpi.com/2075-5309/15/2/197 |
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