Coal-gangue recognition and density prediction via dual-modal data fusion of depth camera and DE-XRT

GUO Yongcun; LUO Qisheng; WANG Shuang; LI Deyong; HE Lei

doi:10.13225/j.cnki.jccs.2025.0289

Abstract：To mitigate the thickness effect of the Dual Energy X-Ray Transmission(DE-XRT)system and achieve accur-ate identification and density prediction of coal and gangue within a wide thickness range,this study thoroughly summar-izes the imaging principle of DE-XRT and fuses depth cameras with DE-XRT at the data level.Based on the size of coal gangue and regional similarity,the DE-XRT images and thickness images were partitioned.By expanding single pixels in-to multi-pixel sets,the two types of information were utilized more accurately and efficiently.Furthermore,using the X-ray and thickness information of each partition as the basis,fused information is calculated via the X-ray energy attenu-ation formula,and then aggregated into block matrices.Multidimensional heterogeneous features were extracted from these block matrices and the DE-XRT images.A robust pre-identification model was then constructed by combining the Relief feature selection algorithm with a Support Vector Machine(SVM)classifier optimized by a GA(Genetic Al-gorithm).Additionally,via transformation calculations of block matrices and statistical analysis of large-scale samples,the fuzzy intervals of coal at various density levels were determined,and a density prediction model based on these intervals was developed.This model considers the impact of using a single formula to calculate coals of different density grades.By establishing fuzzy intervals,the problem of coal density prediction is decomposed into multiple density-grade prediction problems.Meanwhile,the degree of deviation between the target to be predicted and the interval lower limit is used to characterize its proximity to the lower-limit density grade.Experimental results demonstrated that,on a dataset with coal densities ranging from 1.30 to 1.80 g/cm3,gangue densities greater than 1.80 g/cm3,and average thicknesses spanning 5 to 100 mm,the pre-identification model achieved an Pre of 97.522％and an F1 of 0.962.Compared with existing X-ray al-gorithms,grayscale texture methods,and deep learning algorithms,the Pre and F1 were improved by at least 6.433％and 2.888％,respectively.The density prediction model exhibited a mean error not exceeding 5.882％.Specifically,46.993％of targets had prediction errors below 4％,and 93.233％had errors below 10％.The proposed models effectively reduce the impact of the thickness effect on the sorting system,enhance the accuracy of coal-gangue identification and density prediction across a broad thickness range,and provide a theoretical foundation for developing intelligent photoelectric coal-gangue sorting technologies.