Automatic detection of grains in partially recrystallized microstructures using deep learning

Precise identification of recrystallizing grains in partially recrystallized microstructures is essential to obtain quantitative information regarding the recrystallization process. Automatic, robust, user-friendly, and unbiased identification methods that do not rely on hard-coded, preselected values would be highly advantageous. In this study, we test convolutional neural network instance segmentation models to achieve automatic segmentation of individual recrystallizing grains in partially recrystallized microstructures. Our training dataset includes micrographs obtained using electron backscattered diffraction from five alloys with different thermal-mechanical histories and more than 100,000 recrystallizing grains. We adapt and train two state of the art deep learning models, namely Mask R-CNN and PointRend. Both models provide instance segmentation results of good quality, enabling quantitative determination of the microstructural parameters. The PointRend model demonstrates better performance for grains with irregular shapes than Mask R-CNN. Compared to conventional methods, the trained deep learning approach is easier to use, more flexible, and applicable to a wide range of materials. • Deep learning models based on Mask R-CNN and PointRend algorithms were trained for detecting individual recrystallizing grains from EBSD data. • The deep learning instance segmentation methods exhibit good performance across different materials with varying thermo-mechanical processing histories. • Compared with Mask R-CNN, the PointRend model, which has not been previously used for microstructure segmentation, demonstrates better performance for grains with irregular shapes. • In comparison to conventional methods, the trained deep learning approach is easier to use and more flexible after training, since it gives a directly useable output.