The Research Based On Deep Learning Of Coal Gangue Recognition Algorithm

Coal resources are the important basic energy in China.The coal gangue content greatly affects the purity and quality of coal in the process of coal mining and coal washing.Therefore,the separation of coal gangue is of great significance to improve the efficiency of coal automation.The traditional coal gangue separation method is inefficient,polluting the environment and high cost,which can not meet the development needs of today's smart mine.Based on this,this paper summarizes and analyzes the traditional separation methods,and from the perspective of image recognition,puts forward the method of using image texture feature parameters and deep learning technology for coal gangue separation:This paper takes coal and coal gangue collected from Xiangshan Coal Mine in Hancheng,Shaanxi Province as the analysis object,and studies the computer intelligent identification method around the image of coal and coal gangue.The commonly used segmentation methods and traditional texture feature extraction algorithms for gangue image are studied.By homomorphic filtering on the image of coal gangue,the contrast between coal and gangue is enhanced.Then HSV color is obtained by color space conversion,and the image is segmented in HSV color space using K-means++clustering algorithm to get the target image of coal and gangue.Based on this,the texture feature parameters of coal gangue image are analyzed from the angle of image texture feature.The gray symbiosis matrix texture feature parameters and Tamura texture feature parameters are extracted,and the classification and recognition of coal gangue image is achieved by combining BP network and support vector machine.In view of the limitations of traditional machine learning in gangue image sorting,a gangue classification model based on convolution neural network is studied.A deep convolution neural network structure for automatic recognition of coal and gangue is constructed,and automatic classification and recognition of gangue image is achieved by automatically extracting feature parameters.Experiments show that the recognition accuracy of the feature parameters combined with BP neural network and support vector machine is 76.2%and 81.1%respectively.The classification accuracy of the constructed convolution neural network model can reach 93.4%,which can better classify and recognize coal gangue and coal,and realize the automatic classification recognition of coal gangue without characteristic parameters.Compared with the traditional neural network and support vector machine,the convolution neural network structure constructed in this paper effectively improves the accuracy of coal gangue recognition.