Characterization of particle orientation of kaolinite samples using the deep learning-based technique

This paper reports the use of the deep learning-based technique to characterize the particle orientation of clay samples. The U-Net model was applied to perform semantic segmentation for identifying individual kaolinite particles, based on the scanning electron microscopic images taken from clay samples subjected to 1-D consolidation. The measurable elongated particles were manually annotated to facilitate the supervised learning. A fivefold cross-validation was used to ensure satisfactory generalization of the deep learning models. Dice loss and weighted cross-entropy were chosen as the loss functions to tackle the issue of imbalanced classification class. The customized weight maps incorporated in the weight cross-entropy were found effective in forcing the deep learning models to learn how to recognize the particle boundaries. With the trained deep learning models, the measurable elongated kaolinite particles were identified from the ~ 1280 patches within ~ 20 min and the particle directional distribution was quantified using the fabric tensor. The characterization results reveal that the kaolinite particles exhibit a tendency to gradually align along the horizontal plane as imposed by the applied vertical stress. In short, the proposed deep learning-based technique is potential to automate the laborious and visually intensive conventional labeling tasks in fabric characterization of clay samples.