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 segmenta-tion.In this study,we propose a novel image segmentation method derived from mask regional convo-lutional 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 func-tion 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 provide 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.
基金项目
Modern Analysis and Computing Center of China University of Mining and Technology for providing SEM image data of pulverized()
Research and Development Project of Experimental Technology,China University of Mining and Technology(S2023Y018)
国家自然科学基金(62371451)
NETL
NSTL
维普
万方数据