[Objective]The embedded granularity of target minerals refers to their particle size and distribution in ore,which directly influences the design and effectiveness of ore beneficiation processes.Therefore,the measure-ment of embedded granularity of target minerals is a crucial task in process mineralogy research.In traditional process mineralogy research,the main method for observing and analysing ore samples is to use a polarized light mi-croscopy.However,this approach generally suffers from problems such as long processing times,susceptible to subjective factors,and difficulties in achieving automation and large-scale applications.To overcome these limita-tions,this paper proposes a method for detecting the embedded granularity of metal minerals under the microscope based on based on deep learning.[Methods]This method takes specimens from the Shuichang magnetic ore de-posit in Tangshan City,Hebei Province as the object.We take photographs under the reflected light conditions u-sing a polarized light microscope to create a dataset,and design a mineral recognition network model based on the Deeplabv3+network.Thereby it enables automated feature extraction and intelligent recognition of the target metal minerals.This method achieves segmentation of the target minerals by generating binary images of the desired metal minerals.Finally,the analysis and measurement of the embedded granularity of the target metal minerals are com-pleted by combining the maximum Feret diameter.[Results]Compared with traditional manual microscopic meas-urement methods,the application of image measurement based on deep learning in mineral particle analysis has in-creased the processing speed by approximately 119.8 times and the measurement accuracy 169.5 times when meas-uring the same mineral particles,demonstrating its significant improvement in terms of processing efficiency and measurement accuracy.[Conclusion]The deep learning-based method for detecting the embedded granularity of metal minerals under a microscope significantly reduces the detection time and enhances the detection accuracy for mineral embedded granularity.Moreover,it eliminates the influence of subjective factors,which is of great signifi-cance for promoting the intelligent development of process mineralogy.
关键词
深度学习/矿物识别/粒度检测/工艺矿物学/Feret直径/网络模型/自动化特征提取/智能识别
Key words
deep learning/recognition of mineral/particle size detection/process mineralogy/Feret diameter/network model/automated feature extraction/intelligent recognition
维普
万方数据