A Deep Convolutional Neural Network Model for Intelligent Discrimination between Coal and Rocks in Coal Mining Face

Abstract

Accurate identification of the distribution of coal seam is a prerequisite for realizing intelligent mining of shearer. This paper presents a novel method for identifying coal and rock based on a deep convolutional neural network (CNN). Three regularization methods are introduced in this paper to solve the overfitting problem of CNN and speed up the convergence: dropout, weight regularization, and batch normalization. Then the coal-rock image information is enriched by means of data augmentation, which significantly improves the performance. The shearer cutting coal-rock experiment system is designed to collect more real coal-rock images, and some experiments are provided. The experiment results indicate that the network we designed has better performance in identifying the coal-rock images.