  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.
