Measurement of coal-carrying rate in gangue based on three-dimensional image features and gray wolf optimization-support vector machine

Abstract

ABSTRACT To detect the coal-carrying rate in gangue, a new method based on three-dimensional (3D) image features and gray wolf optimization-support vector machine (GWO-SVM) was proposed. First, region segmentation was performed for each sample image according to the particles’ shapes automatically, and each region was identified as coal or gangue. For each region, 4 geometry features and 11 density features were extracted, respectively. In each sample image, the geometry features of the coal region were added by category, while its density features were averaged by category, and the gangue region performed the same operation. The initial feature set, i.e. a two-dimensional (2D) image feature set was obtained by dividing the gangue feature value by the coal feature value. Binocular images were then gathered to extract the third-dimensional (3D) feature, that is, the height feature of the particles. The final feature set consisted of the 2D image features and the third-dimensional feature. Extreme Gradient Boosting was applied to calculate the importance ranking of all the features, and the top nine features were selected as the best features. Then, the coal-carrying rate in gangue was modeled using the SVM optimized by gray wolf optimization. Finally, based on 16 complete features, 8 optimal two-dimensional features and 9 optimal three-dimensional features, three models were established respectively. The results showed that the proposed 3D feature model performed the best with an average relative error of 5.78%.