Coal rock chitin group microscopic image classification method and system based on transfer learning

Abstract

The invention discloses a coal rock chitin group microscopic image classification method and system based on transfer learning, and belongs to the technical field of image processing. The method comprises the following steps: S1, collecting samples and expanding the number of samples; S2, obtaining a pre-trained model; S3, constructing a coal rock chitin group microscopic component identificationmodel; and S4: carrying out component identification. In the step S1, the data enhancement process is as follows: S11, dividing the obtained samples into a training set and a test set; and S12, performing random scaling, random horizontal translation and vertical translation on the images in the training set to realize data expansion. On the basis of the transfer learning method, a target data setis trained by sharing the parameters of a convolution layer and a pooling layer in the pre-trained network, so that a model with good generalization ability can be trained under the condition that the sample size of the chitin group is limited, effective classification of the coal rock chitin images is realized, and the method and the system are worthy of popularization and application.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a method and system for classifying microscopic images of coal-rock exinites based on migration learning. Background technique [0002] The composition of coal rock microcomponents has a great influence on its physical and chemical properties. Therefore, studying the characteristics of coal rock microscopic image components and realizing automatic classification and identification of coal rock microcomponents will play an important role in efficient cleaning of coal rocks. is of great significance. [0003] Coal-rock exinites are rich in hydrocarbons and have a high hydrocarbon-producing capacity, which is closely related to the generation of oil and gas. It affects the performance of coal to a large extent, so the classification and identification of coal-rock exinites is extremely important. research value. [0004] Relevant scholars at home and abroad have also done...

AI Technical Summary

Problems solved by technology

[0006] The technical problem to be solved by the present invention is: how to solve the problem of low recognition rate of existing coal-rock exinite microscopic image classification methods on small data sets, and provide a coal-rock exinite microscopic image based on transfer learning Classification

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