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Real-time ore sorting using color and texture analysis
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Sensor-based ore sorting is a technology used to classify high-grade mineralized rocks from low-grade waste rocks to reduce operation costs.Many ore-sorting algorithms using color images have been pro-posed in the past,but only some validate their results using mineral grades or optimize the algorithms to classify rocks in real-time.This paper presents an ore-sorting algorithm based on image processing and machine learning that is able to classify rocks from a gold and silver mine based on their grade.The algorithm is composed of four main stages:(1)image segmentation and partition,(2)color and tex-ture feature extraction,(3)sub-image classification using neural networks,and(4)a voting system to determine the overall class of the rock.The algorithm was trained using images of rocks that a geologist manually classified according to their mineral content and then was validated using a different set of rocks analyzed in a laboratory to determine their gold and silver grades.The proposed method achieved a Matthews correlation coefficient of 0.961 points,higher than other classification algorithms based on support vector machines and convolutional neural networks,and a processing time under 44 ms,promis-ing for real-time ore sorting applications.
Ore sortingImage color analysisImage texture analysisMachine learning
David G.Shatwell、Victor Murray、Augusto Barton
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Hochschild Mining PLC,La Colonia 180,Lima 15023,Peru
Department of Electrical Engineering,Universidad de Ingenieria y Tecnologia-UTEC,Lima 15063,Peru
John A.Paulson School of Engineering and Applied Sciences,Harvard University,Cambridge 02134,USA
Department of Electrical and Computer Engineering,University of New Mexico,Albuquerque 87131,USA
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维普
