Multi defect detection and analysis of electron microscopy images with deep learning

TL;DR

This paper demonstrates that deep learning, specifically Faster R-CNN, can effectively automate defect detection in electron microscopy images, matching human performance and enabling scalable analysis of large datasets.

Contribution

The study introduces a deep learning approach for defect detection in electron microscopy images, showing its effectiveness with limited training data and potential for automation.

Findings

Deep learning achieves performance comparable to humans.

Faster R-CNN effectively detects multiple defect features.

Method enables scalable, automated microscopy data analysis.

Abstract

Electron microscopy is widely used to explore defects in crystal structures, but human detecting of defects is often time-consuming, error-prone, and unreliable, and is not scalable to large numbers of images or real-time analysis. In this work, we discuss the application of machine learning approaches to find the location and geometry of different defect clusters in irradiated steels. We show that a deep learning based Faster R-CNN analysis system has a performance comparable to human analysis with relatively small training data sets. This study proves the promising ability to apply deep learning to assist the development of automated microscopy data analysis even when multiple features are present and paves the way for fast, scalable, and reliable analysis systems for massive amounts of modern electron microscopy data.