带式输送机置器故障检测算法基于改进的YOLOv5
1School of Intelligent Manufacturing and Modern Industry (School of Mechanical Engineering), Xinjiang University, Ürümqi, 830017, China.
Scientific reports
|January 14, 2025
概括
这项研究引入了一种改进的深度学习方法,用于检测露天煤矿中的输送机置故障. 增强的YOLOv5模型通过准确的实时故障识别来确保更安全的采矿操作.
科学领域:
- 采矿工程 采矿工程 采矿工程
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 由于设备故障,露天煤矿面临安全风险.
- 输送机置机的稳定性对于煤炭生产和工人的安全至关重要.
研究的目的:
- 开发一种基于深度学习的方法,用于实时检测输送机置故障.
- 增强YOLOv5算法,以提高故障检测的准确性和效率.
主要方法:
- 将一个协调注意力机制集成到YOLOv5网络中.
- 用α-CIoU取代传统的CIoU损失,以获得更好的定位精度.
- 在自建的红外图像数据集上进行培训和评估.
主要成果:
- 改进的YOLOv5算法实现了95.3%的平均精度 (mAP).
- 与原来的YOLOv5算法相比,性能提高了2.7%.
- 实时处理速度为每秒285 (FPS).
结论:
- 改进的YOLOv5算法有效地实时检测输送机置缺陷.
- 该方法提高了煤矿环境中的安全性和运营效率.
- 该研究表明了深度学习在工业设备监控方面的潜力.
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