PHCNet: Pyramid Hierarchical-Convolution-Based U-Net for Crack Detection with Mixed Global Attention Module and Edge Feature Extractor
Crack detection plays a vital role in concrete surface maintenance. Deep-learning-based methods have achieved state-of-the-art results. However, these methods have some drawbacks. Firstly, a single-sized convolutional kernel in crack image segmentation tasks may result in feature information loss fo...
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MDPI AG
2023-09-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/18/10263 |
| _version_ | 1797581469914234880 |
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| author | Xiaohu Zhang Haifeng Huang |
| author_facet | Xiaohu Zhang Haifeng Huang |
| author_sort | Xiaohu Zhang |
| collection | DOAJ |
| description | Crack detection plays a vital role in concrete surface maintenance. Deep-learning-based methods have achieved state-of-the-art results. However, these methods have some drawbacks. Firstly, a single-sized convolutional kernel in crack image segmentation tasks may result in feature information loss for small cracks. Secondly, only using linear interpolation or up-sampling to restore high-resolution features does not restore global information. Thirdly, these models are limited to learning edge features, causing edge feature information loss. Finally, various stains interfere with crack feature extraction. To solve these problems, a pyramid hierarchical convolution module (PHCM) is proposed by us to extract the features of cracks with different sizes. Furthermore, a mixed global attention module (MGAM) was used to fuse global feature information. Furthermore, an edge feature extractor module (EFEM) was designed by us to learn the edge features of cracks. In addition, a supplementary attention module (SAM) was used to resolv interference in stains in crack images. Finally, a pyramid hierarchical-convolution-based U-Net (PHCNet) with MGAM, EFEM, and SAM is proposed. The experimental results show that our PHCNet achieves accuracies of 0.929, 0.823, 0.989, and 0.801 on the Cracktree200, CRACK500, CFD, and OAD_CRACK datasets, respectively, which is higher than that of the traditional convolutional models. |
| first_indexed | 2024-03-10T23:04:42Z |
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| id | doaj.art-d9fe138e1ab04968b0483c4c45a58cce |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T23:04:42Z |
| publishDate | 2023-09-01 |
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| spelling | doaj.art-d9fe138e1ab04968b0483c4c45a58cce2023-11-19T09:25:16ZengMDPI AGApplied Sciences2076-34172023-09-0113181026310.3390/app131810263PHCNet: Pyramid Hierarchical-Convolution-Based U-Net for Crack Detection with Mixed Global Attention Module and Edge Feature ExtractorXiaohu Zhang0Haifeng Huang1School of Electronics and Communication Engineering, Sun Yat-sen University, Guangzhou 510275, ChinaSchool of Electronics and Communication Engineering, Sun Yat-sen University, Guangzhou 510275, ChinaCrack detection plays a vital role in concrete surface maintenance. Deep-learning-based methods have achieved state-of-the-art results. However, these methods have some drawbacks. Firstly, a single-sized convolutional kernel in crack image segmentation tasks may result in feature information loss for small cracks. Secondly, only using linear interpolation or up-sampling to restore high-resolution features does not restore global information. Thirdly, these models are limited to learning edge features, causing edge feature information loss. Finally, various stains interfere with crack feature extraction. To solve these problems, a pyramid hierarchical convolution module (PHCM) is proposed by us to extract the features of cracks with different sizes. Furthermore, a mixed global attention module (MGAM) was used to fuse global feature information. Furthermore, an edge feature extractor module (EFEM) was designed by us to learn the edge features of cracks. In addition, a supplementary attention module (SAM) was used to resolv interference in stains in crack images. Finally, a pyramid hierarchical-convolution-based U-Net (PHCNet) with MGAM, EFEM, and SAM is proposed. The experimental results show that our PHCNet achieves accuracies of 0.929, 0.823, 0.989, and 0.801 on the Cracktree200, CRACK500, CFD, and OAD_CRACK datasets, respectively, which is higher than that of the traditional convolutional models.https://www.mdpi.com/2076-3417/13/18/10263convolution neural networkimage segmentationcrack detectionU-Netcrack segmentation |
| spellingShingle | Xiaohu Zhang Haifeng Huang PHCNet: Pyramid Hierarchical-Convolution-Based U-Net for Crack Detection with Mixed Global Attention Module and Edge Feature Extractor Applied Sciences convolution neural network image segmentation crack detection U-Net crack segmentation |
| title | PHCNet: Pyramid Hierarchical-Convolution-Based U-Net for Crack Detection with Mixed Global Attention Module and Edge Feature Extractor |
| title_full | PHCNet: Pyramid Hierarchical-Convolution-Based U-Net for Crack Detection with Mixed Global Attention Module and Edge Feature Extractor |
| title_fullStr | PHCNet: Pyramid Hierarchical-Convolution-Based U-Net for Crack Detection with Mixed Global Attention Module and Edge Feature Extractor |
| title_full_unstemmed | PHCNet: Pyramid Hierarchical-Convolution-Based U-Net for Crack Detection with Mixed Global Attention Module and Edge Feature Extractor |
| title_short | PHCNet: Pyramid Hierarchical-Convolution-Based U-Net for Crack Detection with Mixed Global Attention Module and Edge Feature Extractor |
| title_sort | phcnet pyramid hierarchical convolution based u net for crack detection with mixed global attention module and edge feature extractor |
| topic | convolution neural network image segmentation crack detection U-Net crack segmentation |
| url | https://www.mdpi.com/2076-3417/13/18/10263 |
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