Sandstone pore identification is an important step in studying pore structure.It is difficult to achieve ideal results using general image segmentation algorithms.This paper proposes a method for pore segmentation using EfficientNetV2-S and kernel K-Means clustering.First,a superpixel collection of sandstone images is obtained,and the input tight sandstone images are pre-seg-mented using a superpixel method to construct a library of labeled pore and non-pore images.Then,the EfficientNetV2-S model is applied to extract the semantic features of pores and non-pores in sandstone images.The semantic features are combined with a transfer learning method to learn EfficientNetV2-S model parameters using a limited number of pore and non-pore samples in sandstone images.Finally,a region merging method based on K-Means clustering is designed to construct an objective function by combining the semantic features,grayscale features and edge features of superpixels,which are then merged according to clustering results to obtain a complete pore image.Experiments on sandstone CT images verify the applicability and effectiveness of the pore segmentation method proposed in this paper.
维普
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