Belt conveyor idler fault detection algorithm based on improved YOLOv5
Abstract The rapid expansion of the coal mining industry has introduced significant safety risks, particularly within the harsh environments of open-pit coal mines. The safe and stable operation of belt conveyor idlers is crucial not only for ensuring efficient coal production but also for safeguard...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-81244-x |
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| _version_ | 1832594743347904512 |
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| author | Cen Pan Qing Tao Hao Pei Biao Wang Wei Liu |
| author_facet | Cen Pan Qing Tao Hao Pei Biao Wang Wei Liu |
| author_sort | Cen Pan |
| collection | DOAJ |
| description | Abstract The rapid expansion of the coal mining industry has introduced significant safety risks, particularly within the harsh environments of open-pit coal mines. The safe and stable operation of belt conveyor idlers is crucial not only for ensuring efficient coal production but also for safeguarding the lives of coal mine workers. Therefore, this paper proposes a method based on deep learning for real-time detection of conveyor idler faults. The selected YOLOv5 network is analyzed and improved based on the training results. First, the coordinate attention mechanism is integrated into the model to reassign the weights across different channels. Subsequently, the α-CIoU localization loss function replaces the traditional CIoU to enhance the model’s regression accuracy. Experimental results demonstrate that the enhanced YOLOv5 algorithm achieves a 95.3% mAP on the self-constructed infrared image dataset, surpassing the original algorithm by 2.7%. Moreover, with a processing speed of 285 FPS, it accurately performs the defect detection of conveyor idlers while satisfying real-time operational requirements. |
| format | Article |
| id | doaj-art-17083099d3f64ea2a7e095c9768b67fe |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-17083099d3f64ea2a7e095c9768b67fe2025-01-19T12:21:36ZengNature PortfolioScientific Reports2045-23222025-01-0115111210.1038/s41598-024-81244-xBelt conveyor idler fault detection algorithm based on improved YOLOv5Cen Pan0Qing Tao1Hao Pei2Biao Wang3Wei Liu4School of Intelligent Manufacturing and Modern Industry (School of Mechanical Engineering), Xinjiang UniversitySchool of Intelligent Manufacturing and Modern Industry (School of Mechanical Engineering), Xinjiang UniversitySchool of Intelligent Manufacturing and Modern Industry (School of Mechanical Engineering), Xinjiang UniversitySchool of Intelligent Manufacturing and Modern Industry (School of Mechanical Engineering), Xinjiang UniversitySchool of Intelligent Manufacturing and Modern Industry (School of Mechanical Engineering), Xinjiang UniversityAbstract The rapid expansion of the coal mining industry has introduced significant safety risks, particularly within the harsh environments of open-pit coal mines. The safe and stable operation of belt conveyor idlers is crucial not only for ensuring efficient coal production but also for safeguarding the lives of coal mine workers. Therefore, this paper proposes a method based on deep learning for real-time detection of conveyor idler faults. The selected YOLOv5 network is analyzed and improved based on the training results. First, the coordinate attention mechanism is integrated into the model to reassign the weights across different channels. Subsequently, the α-CIoU localization loss function replaces the traditional CIoU to enhance the model’s regression accuracy. Experimental results demonstrate that the enhanced YOLOv5 algorithm achieves a 95.3% mAP on the self-constructed infrared image dataset, surpassing the original algorithm by 2.7%. Moreover, with a processing speed of 285 FPS, it accurately performs the defect detection of conveyor idlers while satisfying real-time operational requirements.https://doi.org/10.1038/s41598-024-81244-xBelt conveyorsIdlerYOLOv5Attention mechanismα-CIoU |
| spellingShingle | Cen Pan Qing Tao Hao Pei Biao Wang Wei Liu Belt conveyor idler fault detection algorithm based on improved YOLOv5 Scientific Reports Belt conveyors Idler YOLOv5 Attention mechanism α-CIoU |
| title | Belt conveyor idler fault detection algorithm based on improved YOLOv5 |
| title_full | Belt conveyor idler fault detection algorithm based on improved YOLOv5 |
| title_fullStr | Belt conveyor idler fault detection algorithm based on improved YOLOv5 |
| title_full_unstemmed | Belt conveyor idler fault detection algorithm based on improved YOLOv5 |
| title_short | Belt conveyor idler fault detection algorithm based on improved YOLOv5 |
| title_sort | belt conveyor idler fault detection algorithm based on improved yolov5 |
| topic | Belt conveyors Idler YOLOv5 Attention mechanism α-CIoU |
| url | https://doi.org/10.1038/s41598-024-81244-x |
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