中国石油大学学报（自然科学版）2024，Vol.48Issue(2)：118-125,8.DOI:10.3969/j.issn.1673-5005.2024.02.013
基于Unet++网络的数字岩心图像分割泛化能力
Generalization ability analysis of digital rock image segmentation based on Unet+ + network
摘要
Abstract
Image segmentation is an important part of the digital rock technology,and development of deep learning provides a new method for digital rock image segmentation.In this study,the network structure and the amount of training data were deter-mined based on optimized deep learning networks to balance the computational efficiency,and the generalization ability of the network and its influencing factors on different types of rock datasets were discussed.The results show that,among the Unet,Segnet and Unet++ networks,the Unet++ network is the best for the prediction of physical parameters while ensuring the seg-mentation accuracy.The segmentation accuracy of the Unet++ network can reach 98%under the condition that the amount ratio of the training data and the predicted data is 1∶ 1 and the network has two-time samplings.The average segmentation accuracy of different rock images segmented by the trained Unet++ network based on multi-type rocks can reach 95%.Compared with the rock type,the quality of the rock image is more important on the segmentation results of the Unet++ network.关键词
数字岩心/图像分割/深度学习/Unet++/泛化能力Key words
digital rock/image segmentation/deep learning/Unet++/generalization ability分类
能源科技引用本文复制引用
赵久玉,蔡建超..基于Unet++网络的数字岩心图像分割泛化能力[J].中国石油大学学报（自然科学版）,2024,48(2):118-125,8.基金项目
国家自然科学基金项目(42172159) （42172159）
