分割
堆
串联(数学)
深度学习
库存
人工智能
计算机科学
特征(语言学)
工程类
数据挖掘
采矿工程
算法
数学
语言学
哲学
物理
组合数学
核物理学
作者
Zhen Yang,Hao Wu,Hongyan Ding,Junming Liang,Li Guo
标识
DOI:10.1016/j.mineng.2023.108352
摘要
Segmenting blasted stockpile particles in open-pit mines is essential for improving mining operations. However, complex rock textures often challenge traditional segmentation models. This paper presents an enhanced U-Net model that leverages depth-separable convolution and feature depth concatenation to enhance segmentation performance while reducing model complexity and training time. We evaluate our model on a homemade open pit burst pile ore segmentation dataset and report an average accuracy improvement of 1.53 % over the U-Net model. Our work contributes to the field of mining engineering and shows the potential of deep learning methods to improve mining operations.
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