典型文献
Deep learning of rock microscopic images for intelligent lithology identification:Neural network comparison and selection
文献摘要：
An intelligent lithology identification method is proposed based on deep learning of the rock microscopic images.Based on the characteristics of rock images in the dataset,we used Xception,MobileNet_v2,Inception_ResNet_v2,Inception_v3,Densenet121,ResNet101_v2,and ResNet-101 to develop microscopic image classification models,and then the network structures of seven different convolutional neural networks(CNNs)were compared.It shows that the multi-layer representation of rock features can be represented through convolution structures,thus better feature robustness can be achieved.For the loss function,cross-entropy is used to back propagate the weight parameters layer by layer,and the accuracy of the network is improved by frequent iterative training.We expanded a self-built dataset by using transfer learning and data augmentation.Next,accuracy(acc)and frames per second(fps)were used as the evaluation indexes to assess the accuracy and speed of model identification.The results show that the Xception-based model has the optimum performance,with an accuracy of 97.66％in the training dataset and 98.65％in the testing dataset.Furthermore,the fps of the model is 50.76,and the model is feasible to deploy under different hardware conditions and meets the requirements of rapid lithology identification.This proposed method is proved to be robust and versatile in generalization performance,and it is suitable for both geologists and engineers to identify lithology quickly.
文献关键词：
中图分类号：
作者姓名：
Zhenhao Xu;Wen Ma;Peng Lin;Yilei Hua
作者机构：
Geotechnical and Structural Engineering Research Center,Shandong University,Jinan,250061,China;School of Qilu Transportation,Shandong University,Jinan,250061,China;Institute of Robotics and Intelligent Systems,Wuhan University of Science and Technology,Wuhan,430000,China
文献出处：
引用格式：
[1]Zhenhao Xu;Wen Ma;Peng Lin;Yilei Hua-.Deep learning of rock microscopic images for intelligent lithology identification:Neural network comparison and selection)[J].岩石力学与岩土工程学报（英文版）,2022(04):1140-1152
A类：
Densenet121
B类：
Deep,learning,rock,microscopic,images,intelligent,lithology,identification,Neural,comparison,selection,An,method,proposed,deep,Based,characteristics,dataset,used,Xception,MobileNet,v2,Inception,v3,ResNet101,develop,classification,models,then,structures,seven,different,convolutional,neural,networks,CNNs,were,compared,It,shows,that,multi,layer,representation,features,can,represented,through,thus,better,robustness,achieved,For,loss,function,cross,entropy,back,propagate,weight,parameters,by,accuracy,improved,frequent,iterative,training,We,expanded,self,built,using,transfer,augmentation,Next,frames,second,fps,evaluation,indexes,assess,speed,results,has,optimum,performance,testing,Furthermore,feasible,deploy,under,hardware,conditions,meets,requirements,rapid,This,versatile,generalization,suitable,both,geologists,engineers,identify,quickly
AB值：
0.514459
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