A Deep Convolutional Neural Network Model for Intelligent Discrimination between Coal and Rocks in Coal Mining Face. (14th March 2020)

• Record Type: Journal Article
• Authors: Si, Lei; Xiong, Xiangxiang; Wang, Zhongbin; Tan, Chao
• Other Names: Smolka Bogdan Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2020(2020)
• Publication Date: 2020-03-14
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2020/2616510
• Languages: English
• ISSNs: 1024-123X

Abstract

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.