The Combination of Transformer and You Only Look Once for Automatic Concrete Pavement Crack Detection
The real-time detection of cracks is an important part of road maintenance and an important initiative to reduce traffic accidents caused by road cracks. In response to the lack of efficiency of current research results for the real-time detection of road cracks and the low storage and computational...
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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/2076-3417/13/16/9211 |
| _version_ | 1797585632000737280 |
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| author | Xin Zheng Songrong Qian Shaodong Wei Shiyun Zhou Yi Hou |
| author_facet | Xin Zheng Songrong Qian Shaodong Wei Shiyun Zhou Yi Hou |
| author_sort | Xin Zheng |
| collection | DOAJ |
| description | The real-time detection of cracks is an important part of road maintenance and an important initiative to reduce traffic accidents caused by road cracks. In response to the lack of efficiency of current research results for the real-time detection of road cracks and the low storage and computational capacity of edge devices, a new automatic crack detection algorithm is proposed: BT–YOLO. We combined Bottleneck Transformer with You Only Look Once (YOLO), which is more conducive to extracting the features of small cracks than YOLOv5s. The introduction of DWConv to the feature extraction network reduced the number of parameters and improved the inference speed of the network. We embedded the SimAM (Simple, Parameter-Free Attention Module) non-parametric attention mechanism to make the crack features more prominent. The experimental results showed that the accuracy of BT–YOLO in crack detection was increased by 4.5%, the mapped value was increased by 8%, and the parameter amount was decreased by 24.9%. Eventually, we deployed edge devices for testing. The frame rate reached 89, which satisfied the requirements of real-time crack detection. |
| first_indexed | 2024-03-11T00:09:50Z |
| format | Article |
| id | doaj.art-b36bd542e512434784243eb0486febd9 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T00:09:50Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-b36bd542e512434784243eb0486febd92023-11-19T00:06:09ZengMDPI AGApplied Sciences2076-34172023-08-011316921110.3390/app13169211The Combination of Transformer and You Only Look Once for Automatic Concrete Pavement Crack DetectionXin Zheng0Songrong Qian1Shaodong Wei2Shiyun Zhou3Yi Hou4School of Mechanical Engineering, Guizhou University, Guiyang 550025, ChinaSchool of Mechanical Engineering, Guizhou University, Guiyang 550025, ChinaSchool of Mechanical Engineering, Guizhou University, Guiyang 550025, ChinaSchool of Mechanical Engineering, Guizhou University, Guiyang 550025, ChinaSchool of Mechanical Engineering, Guizhou University, Guiyang 550025, ChinaThe real-time detection of cracks is an important part of road maintenance and an important initiative to reduce traffic accidents caused by road cracks. In response to the lack of efficiency of current research results for the real-time detection of road cracks and the low storage and computational capacity of edge devices, a new automatic crack detection algorithm is proposed: BT–YOLO. We combined Bottleneck Transformer with You Only Look Once (YOLO), which is more conducive to extracting the features of small cracks than YOLOv5s. The introduction of DWConv to the feature extraction network reduced the number of parameters and improved the inference speed of the network. We embedded the SimAM (Simple, Parameter-Free Attention Module) non-parametric attention mechanism to make the crack features more prominent. The experimental results showed that the accuracy of BT–YOLO in crack detection was increased by 4.5%, the mapped value was increased by 8%, and the parameter amount was decreased by 24.9%. Eventually, we deployed edge devices for testing. The frame rate reached 89, which satisfied the requirements of real-time crack detection.https://www.mdpi.com/2076-3417/13/16/9211automatic crack detectiontransformerBT–YOLOattention mechanismedge device deployment |
| spellingShingle | Xin Zheng Songrong Qian Shaodong Wei Shiyun Zhou Yi Hou The Combination of Transformer and You Only Look Once for Automatic Concrete Pavement Crack Detection Applied Sciences automatic crack detection transformer BT–YOLO attention mechanism edge device deployment |
| title | The Combination of Transformer and You Only Look Once for Automatic Concrete Pavement Crack Detection |
| title_full | The Combination of Transformer and You Only Look Once for Automatic Concrete Pavement Crack Detection |
| title_fullStr | The Combination of Transformer and You Only Look Once for Automatic Concrete Pavement Crack Detection |
| title_full_unstemmed | The Combination of Transformer and You Only Look Once for Automatic Concrete Pavement Crack Detection |
| title_short | The Combination of Transformer and You Only Look Once for Automatic Concrete Pavement Crack Detection |
| title_sort | combination of transformer and you only look once for automatic concrete pavement crack detection |
| topic | automatic crack detection transformer BT–YOLO attention mechanism edge device deployment |
| url | https://www.mdpi.com/2076-3417/13/16/9211 |
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