Generalization ability analysis of digital rock image segmentation based on Unet+ + network
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.
digital rockimage segmentationdeep learningUnet++generalization ability
国家科技期刊平台
NSTL
万方数据
维普
