An image segmentation method of pulverized coal for particle size analysis

Abstract

An important index to evaluate the process efficiency of coal preparation is the mineral liberation degree of pulverized coal, which is greatly influenced by the particle size and shape distribution acquired by image segmentation. However, the agglomeration effect of fine powders and the edge effect of granular images caused by scanning electron microscopy greatly affect the precision of particle image segmentation. In this study, we propose a novel image segmentation method derived from mask regional convolutional neural network based on deep learning for recognizing fine coal powders. Firstly, an atrous convolution is introduced into our network to learn the image feature of multi-sized powders, which can reduce the missing segmentation of small-sized agglomerated particles. Then, a new mask loss function combing focal loss and dice coefficient is used to overcome the false segmentation caused by the edge effect. The final comparative experimental results show that our method achieves the best results of 94.43% and 91.44% on AP50 and AP75 respectively among the comparison algorithms. In addition, in order to provides an effective method for particle size analysis of coal particles, we study the particle size distribution of coal powders based on the proposed image segmentation method and obtain a good curve relationship between cumulative mass fraction and particle size.