Lightweight Crack Automatic Detection Algorithm Based on TF-MobileNet
With the progress of social life, the aging of building facilities has become an inevitable phenomenon. The efficiency of manual crack detection is limited, so it is necessary to explore intelligent detection technology. This article proposes a novel crack detection method TF-MobileNet. We took into...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-10-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/19/9004 |
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| author | Jiantao Yu Songrong Qian Cheng Chen |
| author_facet | Jiantao Yu Songrong Qian Cheng Chen |
| author_sort | Jiantao Yu |
| collection | DOAJ |
| description | With the progress of social life, the aging of building facilities has become an inevitable phenomenon. The efficiency of manual crack detection is limited, so it is necessary to explore intelligent detection technology. This article proposes a novel crack detection method TF-MobileNet. We took into account the effect of lightweight and crack feature extraction, so we developed a novel crack feature extraction backbone network, which combined Transformer and MobileNetV3. Then we improved the feature fusion network by using the multi-headed attention mechanism of the Bottleneck Transformer, which enables the feature fusion effect to be improved. Then, we integrated SENet and SimAM attention mechanisms into the networks used for feature extraction and feature fusion, thereby further improving the crack detection performance. Finally, we deployed our model in edge devices (NVIDIA Jeston Nano). The findings indicate that our proposed model has achieved 90.8% mAP on the dataset and worked well on the edge device side, which meet the requirements of automatic crack detection. Our model enables real-time monitoring of pavement using edge devices. This approach allows for timely maintenance and repair of the pavement. In the future, we can train the model to recognize more pavement distress features, addressing road safety issues effectively. |
| format | Article |
| id | doaj-art-ee2a3b4b43ab4104938fa583b8bc69d5 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-ee2a3b4b43ab4104938fa583b8bc69d52025-08-20T01:47:44ZengMDPI AGApplied Sciences2076-34172024-10-011419900410.3390/app14199004Lightweight Crack Automatic Detection Algorithm Based on TF-MobileNetJiantao Yu0Songrong Qian1Cheng Chen2School of Mechanical Engineering, Guizhou University, Guiyang 550025, ChinaState Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, ChinaSchool of Mechanical Engineering, Guizhou University, Guiyang 550025, ChinaWith the progress of social life, the aging of building facilities has become an inevitable phenomenon. The efficiency of manual crack detection is limited, so it is necessary to explore intelligent detection technology. This article proposes a novel crack detection method TF-MobileNet. We took into account the effect of lightweight and crack feature extraction, so we developed a novel crack feature extraction backbone network, which combined Transformer and MobileNetV3. Then we improved the feature fusion network by using the multi-headed attention mechanism of the Bottleneck Transformer, which enables the feature fusion effect to be improved. Then, we integrated SENet and SimAM attention mechanisms into the networks used for feature extraction and feature fusion, thereby further improving the crack detection performance. Finally, we deployed our model in edge devices (NVIDIA Jeston Nano). The findings indicate that our proposed model has achieved 90.8% mAP on the dataset and worked well on the edge device side, which meet the requirements of automatic crack detection. Our model enables real-time monitoring of pavement using edge devices. This approach allows for timely maintenance and repair of the pavement. In the future, we can train the model to recognize more pavement distress features, addressing road safety issues effectively.https://www.mdpi.com/2076-3417/14/19/9004TF-MobileNetautomatic crack detectionlightweightingattention mechanismedge deployment |
| spellingShingle | Jiantao Yu Songrong Qian Cheng Chen Lightweight Crack Automatic Detection Algorithm Based on TF-MobileNet Applied Sciences TF-MobileNet automatic crack detection lightweighting attention mechanism edge deployment |
| title | Lightweight Crack Automatic Detection Algorithm Based on TF-MobileNet |
| title_full | Lightweight Crack Automatic Detection Algorithm Based on TF-MobileNet |
| title_fullStr | Lightweight Crack Automatic Detection Algorithm Based on TF-MobileNet |
| title_full_unstemmed | Lightweight Crack Automatic Detection Algorithm Based on TF-MobileNet |
| title_short | Lightweight Crack Automatic Detection Algorithm Based on TF-MobileNet |
| title_sort | lightweight crack automatic detection algorithm based on tf mobilenet |
| topic | TF-MobileNet automatic crack detection lightweighting attention mechanism edge deployment |
| url | https://www.mdpi.com/2076-3417/14/19/9004 |
| work_keys_str_mv | AT jiantaoyu lightweightcrackautomaticdetectionalgorithmbasedontfmobilenet AT songrongqian lightweightcrackautomaticdetectionalgorithmbasedontfmobilenet AT chengchen lightweightcrackautomaticdetectionalgorithmbasedontfmobilenet |