The properties of aluminum alloy highly depend on the distribution, shape, and size of the microstructures. Thus accurate segmentation of these microstructures is crucial in the fields of material science. However, it is often challenging due to large variations in microstructural appearance and insufficiency in hand-labeled data. To address these challenges, we propose a hierarchical parameter transfer learning method for the automatic segmentation of microstructures in aluminum alloy micrograph, which can be seen as the generalization of the typical parameter transfer method. In the proposed method, we use the multilayer structure, multinetwork structure, and retraining technology. It can make full use of the advantages of different networks and transfer network parameters in the order from high transferability to low transferability. Several experiments are presented to verify the effectiveness of the proposed method. Our method achieves 98.88% segmentation accuracy and outperforms four typical segmentation methods.
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