Research on coal rock recognition algorithm and “self-learning” model

Abstract

Aiming at the problems of poor effect, poor stability and small application range of coal and rock recognition methods in fully mechanized coal mining face, based on the difference between the basic characteristics of coal and rock, from the visual differences of image edge and gray threshold, with the help of clustering theory, image processing is used to analyze the boundary of coal and rock image, and the textural feature information contained in coal and rock gray level co-occurrence matrix is analyzed in this paper, two image recognition models of coal rock boundary are proposed: the “similarity” measurement estimation model of image gray scale and the Gaussian mixture clustering recognition model; and a coal rock mixed model is constructed. It provides a reference for the accurate identification of coal and rock in working face and the reduction of faults and safety problems caused by shearer cutting rock.

Keywords

• coal and rock intelligent recognition
• image processing algorithm
• self-learning model
• clustering analysis
• textural feature extraction