Belt conveyor idler fault detection algorithm based on improved YOLOv5

• Published in: Scientific reports Vol. 15; no. 1; pp. 1926 - 12
• Main Authors: Pan, Cen, Tao, Qing, Pei, Hao, Wang, Biao, Liu, Wei
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 14.01.2025; Nature Publishing Group; Nature Portfolio
• Subjects: 639/166; 639/705/794; Algorithms; Attention mechanism; Belt conveyors; Coal; Coal industry; Coal mines; Coal mining; Deep learning; Harsh environments; Humanities and Social Sciences; Idler; Localization; Mining accidents & safety; multidisciplinary; Science; Science (multidisciplinary); YOLOv5; α-CIoU; Belt conveyors; Attention mechanism; YOLOv5; α-CIoU; Idler
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-81244-x

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.