A pellet size recognition model combining Mask-RCNN and least squares method
In blast furnace smelting,the particle size distribution of pellets is one of the important indicators for evaluating their quality.A uniformly sized pellet ore helps improve the permeability of burden in the blast furnace and reduce smelting energy consumption.Pellet edge segmentation and particle size analysis were employed by the Mask-RCNN(Mask Regional Convolutional Neural Network)algorithm.Aiming at the interference of severe stacking of pellets on image recognition,according to the contour characteristics of pellets,the concave point detection algorithm is used to obtain the feature points on the contour and classify the pellets with different degrees of sticky stacking,and combined with the least-squares circle-fitting method to recover the occluded contour information.The research results indicate that the average accuracy of instance segmentation in the Mask-RCNN algorithm can reach over 93.5％.However,due to the stacking effect of pellet particles,the particle size distribution curve detected by the Mask-RCNN algorithm deviates significantly from the artificial sieving curve.After improving the concave point detection algorithm and the least squares circle fitting algorithm,the proportion of pellets smaller than 16mm increased to varying degrees,resulting in particle size distribution curves from image detection aligning completely with those obtained through artificial sieving.The average particle size error was reduced by 5.52％on the basis of the Mask-RCNN algorithm,with a reduction range of 98.6％.
pellet sizeimage recognitionMask-RCNNleast squares circle fittingconcave point detection
NETL
NSTL
万方数据
