Automated and Refined Application of Convolutional Neural Network Modeling to Metallic Powder Particle Satellite Detection

Research concerned with the identification as well as quantification of satellites found within metallic powders has recently demonstrated the promise of implementing Mask R-CNNs, instance segmentation, and transfer learning. Though the original research and development of such an approach demonstrated the functionality of the data-driven image analysis framework, questions remained in regards to the scale-ability of the Mask R-CNN-based model. Accordingly, the present work demonstrates the fact that the originally formulated model can be expanded to include scanning electron micrographs to various powder types at variate magnifications (rather than the original case of micrographs of a single powder type at a single magnification). Moreover, the present work establishes a process that enables users to specifically target which images will have most impact on increasing generalize-ability and performance in order to optimize maximum improvement of the model with the least amount of images annotated. Beyond this, we also outline a method of auto-labeling satellites in images by using a trained model to increase its own training set size.