Enhanced U-Net model for rock pile segmentation and particle size analysis

Abstract

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.