Coal and gangue recognition based on improved support vector machine

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%.