PSNet: Parallel-Convolution-Based U-Net for Crack Detection with Self-Gated Attention Block
Crack detection is an important task for road maintenance. Currently, convolutional neural-network-based segmentation models with attention blocks have achieved promising results, for the reason that these models can avoid the interference of lights and shadows. However, by carefully examining the s...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/17/9875 |
| _version_ | 1827728227365289984 |
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| author | Xiaohu Zhang Haifeng Huang |
| author_facet | Xiaohu Zhang Haifeng Huang |
| author_sort | Xiaohu Zhang |
| collection | DOAJ |
| description | Crack detection is an important task for road maintenance. Currently, convolutional neural-network-based segmentation models with attention blocks have achieved promising results, for the reason that these models can avoid the interference of lights and shadows. However, by carefully examining the structure of these models, we found that these segmentation models usually use down-sampling operations to extract high-level features. This operation reduces the resolution of features and causes feature information loss. Thus, in our proposed method, a Parallel Convolution Module (PCM) was designed to avoid feature information loss caused by down-sampling. In addition, the attention blocks in these models only focused on selecting channel features or spatial features, without controlling feature information flow. To solve the problem, a Self-Gated Attention Block (SGAB) was used to control the feature information flow in the attention block. Therefore, based on the ideas above, a PSNet with a PCM and SGAB was proposed by us. Additionally, as there were few public datasets for detailed evaluation of our method, we collected a large dataset by ourselves, which we named the OAD_CRACK dataset. Compared with the state-of-the-art crack detection method, our proposed PSNet demonstrated competitive segmentation performance. The experimental results showed that our PSNet could achieve accuracies of 92.6%, 81.2%, 98.5%, and 76.2% against the Cracktree200, CRACK500, CFD, and OAD_CRACK datasets, respectively, which were 2.6%, 4.2%, 1.2%, and 3.3% higher than those of the traditional attention models. |
| first_indexed | 2024-03-10T23:27:21Z |
| format | Article |
| id | doaj.art-11733f6ccfc546d8ae71ecc8f1607ec4 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T23:27:21Z |
| publishDate | 2023-08-01 |
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| series | Applied Sciences |
| spelling | doaj.art-11733f6ccfc546d8ae71ecc8f1607ec42023-11-19T07:52:54ZengMDPI AGApplied Sciences2076-34172023-08-011317987510.3390/app13179875PSNet: Parallel-Convolution-Based U-Net for Crack Detection with Self-Gated Attention BlockXiaohu 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 is an important task for road maintenance. Currently, convolutional neural-network-based segmentation models with attention blocks have achieved promising results, for the reason that these models can avoid the interference of lights and shadows. However, by carefully examining the structure of these models, we found that these segmentation models usually use down-sampling operations to extract high-level features. This operation reduces the resolution of features and causes feature information loss. Thus, in our proposed method, a Parallel Convolution Module (PCM) was designed to avoid feature information loss caused by down-sampling. In addition, the attention blocks in these models only focused on selecting channel features or spatial features, without controlling feature information flow. To solve the problem, a Self-Gated Attention Block (SGAB) was used to control the feature information flow in the attention block. Therefore, based on the ideas above, a PSNet with a PCM and SGAB was proposed by us. Additionally, as there were few public datasets for detailed evaluation of our method, we collected a large dataset by ourselves, which we named the OAD_CRACK dataset. Compared with the state-of-the-art crack detection method, our proposed PSNet demonstrated competitive segmentation performance. The experimental results showed that our PSNet could achieve accuracies of 92.6%, 81.2%, 98.5%, and 76.2% against the Cracktree200, CRACK500, CFD, and OAD_CRACK datasets, respectively, which were 2.6%, 4.2%, 1.2%, and 3.3% higher than those of the traditional attention models.https://www.mdpi.com/2076-3417/13/17/9875convolutional neural networkimage segmentationcrack detectionU-Netcrack segmentation |
| spellingShingle | Xiaohu Zhang Haifeng Huang PSNet: Parallel-Convolution-Based U-Net for Crack Detection with Self-Gated Attention Block Applied Sciences convolutional neural network image segmentation crack detection U-Net crack segmentation |
| title | PSNet: Parallel-Convolution-Based U-Net for Crack Detection with Self-Gated Attention Block |
| title_full | PSNet: Parallel-Convolution-Based U-Net for Crack Detection with Self-Gated Attention Block |
| title_fullStr | PSNet: Parallel-Convolution-Based U-Net for Crack Detection with Self-Gated Attention Block |
| title_full_unstemmed | PSNet: Parallel-Convolution-Based U-Net for Crack Detection with Self-Gated Attention Block |
| title_short | PSNet: Parallel-Convolution-Based U-Net for Crack Detection with Self-Gated Attention Block |
| title_sort | psnet parallel convolution based u net for crack detection with self gated attention block |
| topic | convolutional neural network image segmentation crack detection U-Net crack segmentation |
| url | https://www.mdpi.com/2076-3417/13/17/9875 |
| work_keys_str_mv | AT xiaohuzhang psnetparallelconvolutionbasedunetforcrackdetectionwithselfgatedattentionblock AT haifenghuang psnetparallelconvolutionbasedunetforcrackdetectionwithselfgatedattentionblock |