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Generic dual-phase classification models through deep learning semantic segmentation method and image gray-level optimization
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DOI：10.1016/j.scriptamat.2023.115948.png)
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Abstract
En 中文
Two generic deep learning models for automatic classification of dual-phase microstructures were constructed through the semantic segmentation method of DeepLab v3+, based on a small data including 6 SEM images of the U-2Nb alloys with dual-phase microstructure and the corresponding ground truth. One model is suitable for directly classifying images with different gray-level ranges and has a good average classifying accuracy. Although the other model is only suitable for a specific gray-level range, but it cost less for training and can realize a higher classifying accuracy than the former model combined with optimal gray-level adjustment for images. The two DL models were applied to classify dual-phase microstructures of different morphologies and materials, including the isothermal cooling and continuous cooling microstructures of U-2Nb alloys, and the dual-phase steel microstructure, the results of which manifest that both the two models have high classification accuracy and excellent general applicability.
Keywords:
Dual-phase
Microstructure classification
Semantic segmentation
Deep learning
Gray-level range
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5.6
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5.1W
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