ArticleScientific reports2025
Belt conveyor idler fault detection algorithm based on improved YOLOv5.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Sequential Topology Optimization and Generative Design with Finite Element Validation for Lightweight PA6 Roller Conveyor Components.Materials (Basel, Switzerland) · 2026Article
- Sensor-Based Channel-Wise Remaining Useful Life Estimation and Multi-Threshold Maintenance Policy for Steel-Cord Conveyor Belts.Sensors (Basel, Switzerland) · 2026Article
- Computer Vision-Based Techniques for Conveyor Belt Condition Monitoring: A Systematic Review.Sensors (Basel, Switzerland) · 2026Review
- Real-time detection of WeChat moments interface blocking behavior and generation of generational user personas based on YOLOv5.Scientific reports · 2026Article
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Authors and funding
5 authors.
Funding
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.
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Registered trials
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