Multi defect detection and analysis of electron microscopy images with deep learning
Multi defect detection and analysis of electron microscopy images with deep learning
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DOI：
10.1016/j.commatsci.2021.110576
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发表时间：
2021-08
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通讯作者：
Mingren Shen;Guanzhao Li;Dongxian Wu;Yuhan Liu;J. Greaves;Wei Hao;Nathaniel J. Krakauer;Leah Krudy;J. Perez;V. Sreenivasan;Bryan Sanchez;Oigimer Torres;Wei Li;K. Field;D. Morgan
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作者：
Mingren Shen;Guanzhao Li;Dongxian Wu;Yuhan Liu;J. Greaves;Wei Hao;Nathaniel J. Krakauer;Leah Krudy;J. Perez;V. Sreenivasan;Bryan Sanchez;Oigimer Torres;Wei Li;K. Field;D. Morgan
电子显微镜被广泛用于探索晶体结构中的缺陷，但人工检测缺陷通常耗时，容易出错且不可靠，并且无法扩展到大量图像或实时分析。在这项工作中，我们讨论了机器学习方法的应用，找到辐照钢中不同缺陷簇的位置和几何形状。我们证明了基于深度学习的Faster R-CNN分析系统在相对较小的训练数据集下具有与人类分析相当的性能。这项研究证明了应用深度学习来协助自动显微镜数据分析的发展的有前途的能力，即使存在多种特征，并为大量现代电子显微镜数据的快速，可扩展和可靠的分析系统铺平了道路。
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.