Title: Deep learning-based image classification of gas coal

Authors: Zelin Zhang; Zhiwei Zhang; Yang Liu; Lei Wang; Xuhui Xia

Addresses: Key Laboratory of Metallurgical Equipment and Control Technology, Wuhan University of Science and Technology, Wuhan 430081, Hubei, China; Hubei Key Laboratory for Efficient Utilisation and Agglomeration of Metallurgic Mineral Resources, School of Resource and Environmental Engineering, Wuhan University of Science and Technology, Wuhan 430081, Hubei, China ' Hubei Key Laboratory for Efficient Utilisation and Agglomeration of Metallurgic Mineral Resources, School of Resource and Environmental Engineering, Wuhan University of Science and Technology, Wuhan 430081, Hubei, China ' Hubei Key Laboratory for Efficient Utilisation and Agglomeration of Metallurgic Mineral Resources, School of Resource and Environmental Engineering, Wuhan University of Science and Technology, Wuhan 430081, Hubei, China ' Key Laboratory of Metallurgical Equipment and Control Technology, Wuhan University of Science and Technology, Wuhan 430081, Hubei, China ' Key Laboratory of Metallurgical Equipment and Control Technology, Wuhan University of Science and Technology, Wuhan 430081, Hubei, China

Abstract: Machine vision-based sorting technology is a potential mineral separation method with the merits of high cost performance and security. However, the classification accuracy of common mineral pattern recognition methods is not satisfactory. Therefore, this paper proposes a deep learning-based classification method for the multi-class prediction of gas coal in the machine vision sorting system. The single coal images are obtained by the image segmentation algorithm and taken as the inputs of deep learning model based on the Inception_v3 network with the aid of transfer learning. The cross-entropy loss function and test accuracy are applied as the evaluation indexes of the classification effect. The result derived using the proposed model indicates that the classification accuracies of four-products, three-products and two-products are up to 82.3%, 87.8% and 99.4%, respectively. Additionally, a grad-cam convolution thermal map is used to visualise the differences in the deep learning classification process. Finally, the proposed deep learning model proves its superiority over the traditional models such as Back Propagation Neural Network (BP), Random Forest (RF) and Support Vector Machine (SVM).

Keywords: deep learning; gas coal; image classification; transfer learning.

DOI: 10.1504/IJGEI.2021.117027

International Journal of Global Energy Issues, 2021 Vol.43 No.4, pp.371 - 386

Received: 29 Apr 2020
Accepted: 28 Sep 2020
Published online: 12 Aug 2021 *

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