首页|Microstructure recognition of steels by machine learning based on visual attention mechanism
Microstructure recognition of steels by machine learning based on visual attention mechanism
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万方数据
维普
U-Net has achieved good performance with the small-scale datasets through skip connections to merge the features of the low-level layers and high-level layers and has been widely utilized in biomedical image segmentation as well as recent microstructure image segregation of the materials.Three representative visual attention mechanism modules,named as squeeze-and-excitation networks,convolutional block attention module,and extended calibration algorithm,were intro-duced into the traditional U-Net architecture to further improve the prediction accuracy.It is found that compared with the original U-Net architecture,the evaluation index of the improved U-Net architecture has been significantly improved for the microstructure segmentation of the steels with the ferrite/martensite composite microstructure and pearlite/ferrite composite microstructure and the complex martensite/austenite island/bainite microstructure,which demonstrates the advantages of the utilization of the visual attention mechanism in the microstructure segregation.The reasons for the accuracy improvement were discussed based on the feature maps analysis.
The State Key Laboratory of Refractories and Metallurgy,Hubei Province Key Laboratory of Systems Science on Metallurgical Processing,International Research Institute for Steel Technology,Collaborative Center on Advanced Steels,Wuhan University of Science and Technology,Wuhan 430081,Hubei,China
Metals Valley and Band(Foshan)Metallic Composite Materials Co.,Ltd.,Foshan 528000,Guangdong,China
万方数据
维普
