一种基于传感器的废岩探测方法,用于低光环境下的铜矿
Jianing Ding1,2, Fuming Qu1, Weihua Zhou1,2
1Institute of Minerals Research, University of Science and Technology Beijing, Beijing 100083, China.
Sensors (Basel, Switzerland)
|October 16, 2025
概括
这项研究引入了一个深度学习算法,以改善铜矿废物岩石在低光条件下检测. 改进的检测系统提高了分类的准确性和效率,这对于采矿环境管理至关重要.
科学领域:
- 采矿工程 采矿工程 采矿工程
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 铜矿开采产生大量的废石,影响土地利用和环境.
- 使用视觉传感器的智能分类系统是有效的废岩管理的关键.
- 排序环境中的不良照明条件阻碍了基于视觉的检测准确性.
研究的目的:
- 开发一种深度学习算法,用于在低光环境中准确地检测铜矿废岩.
- 在具有挑战性的工业环境中提高基于视觉的检测系统的性能.
- 为了提高铜矿废物岩石分类的效率和成本效益.
主要方法:
- 提出了一种基于深度学习的算法,包括用于图像预处理的照明适应变压器 (IAT) 模块.
- 集成了局部增强-全球调制 (LEGM) 模块,以提高Neck架构内的检测准确性.
- 使用MPDIoU损失函数优化对象检测模型以提高性能.
主要成果:
- 拟议的算法实现了0.957.5的平均平均精度 (mAP@0.5).
- 该算法达到0.689的mAP@0.5:0.95,显著超过现有的先进方法.
- 在低光条件下检测铜矿废物岩石的表现卓越.
结论:
- 开发的深度学习算法有效地解决了铜矿废物岩石检测中低光挑战.
- 集成IAT和LEGM模块,以及MPDIoU损失,显著提高了分类精度和效率.
- 这一进步提供了一种具有成本效益的解决方案,用于改善铜矿运营中的环境管理.
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