Coal and gangue recognition based on improved support vector machine

Yu Sun; Yehui Xie; Jincai Zhang

doi:10.1117/12.2626558

20 December 2021 Coal and gangue recognition based on improved support vector machine

Yu Sun, Yehui Xie, Jincai Zhang

Proceedings Volume 12155, International Conference on Computer Vision, Application, and Design (CVAD 2021); 1215514 (2021) https://doi.org/10.1117/12.2626558
Event: International Conference on Computer Vision, Application, and Design (CVAD 2021), 2021, Sanya, China

Abstract

In the process of coal mining, the separation of coal and gangue is a very important step. Traditional coal preparation methods include manual coal preparation, heavy medium coal preparation, ray projection coal preparation, etc. these methods can not separate coal and gangue under the condition of safety and speed at the same time. Therefore, to improve the recognition rate of coal gangue separation, a coal gangue recognition method based on improved Support Vector Machine is proposed in this paper. First, the images of the coal and gangue are preprocessed. Then, the gray and texture features of the coal and gangue are extracted from the preprocessed images. Finally, each feature vector is input into the Support Vector Machine model optimized by Fruit Fly for recognition and classification. The experimental results show that the accuracy is 96.33%.

Citation Download Citation

Yu Sun, Yehui Xie, and Jincai Zhang "Coal and gangue recognition based on improved support vector machine", Proc. SPIE 12155, International Conference on Computer Vision, Application, and Design (CVAD 2021), 1215514 (20 December 2021); https://doi.org/10.1117/12.2626558

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