Deep Learning for Semantic Segmentation of Defects in Advanced STEM Images of Steels.

ABSTRACT: Crystalline materials exhibit long-range ordered lattice unit, within which resides nonperiodic structural features called defects. These crystallographic defects play a vital role in determining the physical and mechanical properties of a wide range of material systems. While computer vision has demonstrated success in recognizing feature patterns in images with well-defined contrast, automated identification of nanometer scale crystallographic defects in electron micrographs governed by complex contrast mechanisms is still a challenging task. Here, building upon an advanced defect imaging mode that offers high feature clarity, we introduce DefectSegNet - a new convolutional neural network (CNN) architecture that performs semantic segmentation of three common crystallographic defects in s

SUBMITTER: Roberts G 

PROVIDER: S-EPMC6726638 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature