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使用LODM在爆炸堆中检测大型矿石

Xingfan Zhang^1,2, Hongdi Jing³, Miao Yu⁴

¹School of Resources and Security Engineering, University of Science and Technology Beijing, Beijing, 100083, China.

Scientific reports

|October 6, 2025

概括

这项研究引入了一种新的大型矿石检测和测量 (LODM) 模型,用于露天矿山. 使用Mask R-CNN和ResNet34的LODM模型准确地识别了矿石碎片,提高了采矿效率.

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:

有效的矿石碎片化分析对于优化开采矿业务至关重要.
传统的图像细分方法与复杂的矿石堆特征作斗争.

研究的目的:

开发和评估一个强大的大型矿石检测和测量 (LODM) 模型,用于分析爆炸矿石碎片化.
为了提高矿业中矿石尺寸检测的准确性和效率.

主要方法:

提出了一种基于Mask R-CNN的大型矿石检测和测量 (LODM) 模型.
在MPBRD1.0数据集上训练和评估模型.
将LODM与K-means,Canny,流域和U-Net算法进行比较.
研究ResNet34作为特征提取的支柱,与ResNet50,ResNet101和VGG16.6进行比较.

主要成果:

与传统算法和U-Net相比,LODM模型表现出优异的性能.
功能提取网络ResNet34优化了LODM模型的性能.
检测结果与实际的矿石碎片化条件密切一致.

结论:

拟议的LODM模型显著提高了在露天矿山中检测大型矿石碎片的性能.
使用ResNet34作为骨干增强了模型的检测能力.
精确的矿石碎片测量有助于优化二次破碎,装载和运输过程.

关键词:

深度学习是一种深度学习.图像细分图像细分图像细分洛德姆 (LODMDM) 是一个非常有价值的企业.大型矿石是一种大型矿石.面具 R-CNN 的意思

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