Accurate quantification of dislocation loops in complex functional alloys enabled by deep learning image analysis.

In-depth statistics of individual defects observed during transmission electron microscopy (TEM) experiments are essential for the thorough characterization of materials. In this study, we aim to quantitatively characterize the population of dislocation loops in ion-irradiated CrFeMnNi alloys. To this end, we propose an efficient guideline to prepare TEM micrographs dataset for deep learning analysis, adapted for accurate characterization of microstructures produced by thousands of overlapping defects, a very common situation in TEM images, unfeasible by previous existing methods. To reduce human effort, we annotate only a few images and complement the database through a two-step process: initially, singular value decomposition to normalize image background, followed by a controlled data augmentation. The performed analysis provides precise quantitative information about the number of loops of different types, as well as their spatial distribution, their size, and the inter-object distances. These characteristics provide insights into the nucleation, mobility, and growth of dislocation loops, as well as the elastic anisotropy of the material. Our results emphasize how accurate analysis of complex microstructures can provide insights into the physical properties of materials and open up many perspectives for attaining quantitative information on materials properties based solely on their image analysis.