Crack Detection of Track Slab Based on RSG-YOLO
The surface cracks on high-speed railway ballastless track slabs directly influence their lifespan, while the efficiency of damage detection and maintenance is crucial for ensuring operational safety. Leveraging deep learning image processing technology can significantly enhance detection efficiency...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10296906/ |
| _version_ | 1797628981417082880 |
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| author | Tangbo Bai Baile Lv Ying Wang Jialin Gao Jian Wang |
| author_facet | Tangbo Bai Baile Lv Ying Wang Jialin Gao Jian Wang |
| author_sort | Tangbo Bai |
| collection | DOAJ |
| description | The surface cracks on high-speed railway ballastless track slabs directly influence their lifespan, while the efficiency of damage detection and maintenance is crucial for ensuring operational safety. Leveraging deep learning image processing technology can significantly enhance detection efficiency. Therefore, in response to the specific attributes of ballastless track slab crack detection, this paper introduces the RSG-YOLO model. By implementing a reparameterized dual-fused feature pyramid structure, we bolster the network’s feature extraction capacity and curtail the loss of crack features during extraction. SIoU is used to replace CIoU to optimize the bounding box regression loss function, reduce the degrees of freedom of the loss function, and improve the convergence speed The GAM attention mechanism is integrated to heighten the model’s responsiveness to diverse channel information. The proposed RSG-YOLO model was evaluated against mainstream models in the field of crack detection. The results demonstrated improved detection accuracy and recall rates. Specifically, when compared to baseline models, our approach exhibited significant advancements in reducing both missed detections and false alarms. These improvements were quantified by a 4.34% increase in crack detection accuracy and a 3.08% rise in mAP_0.5. Consequently, the RSG-YOLO model effectively enables precise detection of track slab cracks. |
| first_indexed | 2024-03-11T10:47:54Z |
| format | Article |
| id | doaj.art-489ab7c9941f4579acbd14f2cea81ae8 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-11T10:47:54Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-489ab7c9941f4579acbd14f2cea81ae82023-11-14T00:00:32ZengIEEEIEEE Access2169-35362023-01-011112400412401310.1109/ACCESS.2023.332791010296906Crack Detection of Track Slab Based on RSG-YOLOTangbo Bai0https://orcid.org/0000-0002-0841-035XBaile Lv1https://orcid.org/0009-0000-3611-7559Ying Wang2Jialin Gao3Jian Wang4School of Mechanical-Electronic and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, Beijing, ChinaSchool of Mechanical-Electronic and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, Beijing, ChinaBeijing Institute of Aerospace Control Device, Beijing, ChinaSchool of Mechanical-Electronic and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, Beijing, ChinaSchool of Mechanical-Electronic and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, Beijing, ChinaThe surface cracks on high-speed railway ballastless track slabs directly influence their lifespan, while the efficiency of damage detection and maintenance is crucial for ensuring operational safety. Leveraging deep learning image processing technology can significantly enhance detection efficiency. Therefore, in response to the specific attributes of ballastless track slab crack detection, this paper introduces the RSG-YOLO model. By implementing a reparameterized dual-fused feature pyramid structure, we bolster the network’s feature extraction capacity and curtail the loss of crack features during extraction. SIoU is used to replace CIoU to optimize the bounding box regression loss function, reduce the degrees of freedom of the loss function, and improve the convergence speed The GAM attention mechanism is integrated to heighten the model’s responsiveness to diverse channel information. The proposed RSG-YOLO model was evaluated against mainstream models in the field of crack detection. The results demonstrated improved detection accuracy and recall rates. Specifically, when compared to baseline models, our approach exhibited significant advancements in reducing both missed detections and false alarms. These improvements were quantified by a 4.34% increase in crack detection accuracy and a 3.08% rise in mAP_0.5. Consequently, the RSG-YOLO model effectively enables precise detection of track slab cracks.https://ieeexplore.ieee.org/document/10296906/High speed railwaytrack slab cracksYOLOcrack detectionimage processing |
| spellingShingle | Tangbo Bai Baile Lv Ying Wang Jialin Gao Jian Wang Crack Detection of Track Slab Based on RSG-YOLO IEEE Access High speed railway track slab cracks YOLO crack detection image processing |
| title | Crack Detection of Track Slab Based on RSG-YOLO |
| title_full | Crack Detection of Track Slab Based on RSG-YOLO |
| title_fullStr | Crack Detection of Track Slab Based on RSG-YOLO |
| title_full_unstemmed | Crack Detection of Track Slab Based on RSG-YOLO |
| title_short | Crack Detection of Track Slab Based on RSG-YOLO |
| title_sort | crack detection of track slab based on rsg yolo |
| topic | High speed railway track slab cracks YOLO crack detection image processing |
| url | https://ieeexplore.ieee.org/document/10296906/ |
| work_keys_str_mv | AT tangbobai crackdetectionoftrackslabbasedonrsgyolo AT bailelv crackdetectionoftrackslabbasedonrsgyolo AT yingwang crackdetectionoftrackslabbasedonrsgyolo AT jialingao crackdetectionoftrackslabbasedonrsgyolo AT jianwang crackdetectionoftrackslabbasedonrsgyolo |