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

ABSTRACT: 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 a

SUBMITTER: Bilyk T 

PROVIDER: S-EPMC11502901 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature