Shape selection recognition and scattering distribution prediction of adhesion targets in multi-scale dual-energy X-ray images of coal and gangue

Abstract

ABSTRACT Identifying and predicting the distribution of scattered coal and gangue is the premise of locating them. By analyzing the shape of multi-scale coal and gangue, an approach for shape selection and recognition is provided. The issues of independent and adhesion target recognition, adhesion type recognition, and distribution prediction have been resolved. The binary target, circumscribed convex hull, and concave defect images are used to extract a total of 27 shape features. The ReliefF algorithm is employed to select features. The shape recognition model 1 shows the highest adhesion and independent recognition rates of 98.54%. For adhesion kinds, shape recognition model 2 gets the highest recognition accuracy of 92%. According to the experimental findings, the difference between predicted and actual results for the parameters describing the dispersed distribution of targets, such as target number, distribution overlap rate, and distribution density, is less than 4.1%, which is acceptable for use in real-world scenarios.