Automatic Identification of Coal and Rock/Gangue Based on DenseNet and Gaussian Process

TL;DR

This paper presents a novel image-based method combining DenseNet and Gaussian process for automatic identification of coal and rock/gangue, enhancing automation and safety in underground coal mining.

Contribution

The paper introduces a hybrid model that leverages DenseNet for feature extraction and Gaussian process for classification, effective in few-shot learning scenarios.

Findings

High accuracy on underground images

Effective in few-shot learning

Potential for automation in coal mines

Abstract

To improve the purity of coal and prevent damage to the coal mining machine, it is necessary to identify coal and rock in underground coal mines. At the same time, the mined coal needs to be purified to remove rock and gangue. These two procedures are manually operated by workers in most coal mines. The realization of automatic identification and purification is not only conducive to the automation of coal mines, but also ensures the safety of workers. We discuss the possibility of using image-based methods to distinguish them. In order to find a solution that can be used in both scenarios, a model that forwards image feature extracted by DenseNet to Gaussian process is proposed, which is trained on images taken on surface and achieves high accuracy on images taken underground. This indicates our method is powerful in few-shot learning such as identification of coal and rock/gangue and might be beneficial for realizing automation in coal mines.