Multi defect detection and analysis of electron microscopy images with deep learning
This work addresses the time-consuming and error-prone nature of human defect detection in electron microscopy, offering a scalable solution for automated analysis in materials science.
The paper tackled the problem of detecting and analyzing multiple defects in electron microscopy images of irradiated steels, showing that a deep learning-based Faster R-CNN system achieves performance comparable to human analysis with relatively small training datasets.
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.