A Study of Multispectral Technology and Two-Dimension Autoencoder for Coal and Gangue Recognition

Abstract

Coal is one of the main sources of human energy. In the process of coal mining, separating gangue from coal has great significance for environmental protection and energy conservation. The core problem in the separation of gangue is the recognition of coal and gangue. In this study, multispectral technology was used to identify gangue. First, set up a data acquisition system in the laboratory, and then collect spectral data. Coal and gangue spectral data were collected in 202 and 201 groups, respectively. Secondly, design a spectral data dimension reduction model called two-dimension autoencoder(2D-AE). Finally, Random Forest was used to recognize coal and gangue. Meanwhile, CART, KNN, SVM, and AdaBoost were also used for gangue identification. The experimental results show that the maximum average accuracy by 2D-AE combined with RF was the largest, which was 98.89%. Also, the accuracy of gangue recognition is different for spectral images of different wavelengths. This paper mainly studies the recognition of coal and gangue based on multispectral technology, which is of great significance for the next step of detecting gangue based on the technology.

Keywords

• Gangue recognition
• multispectral
• two-dimension autoencoder
• random forest