自动工具能够从仅仅几张电子显微镜图像中识别出缺陷。电子显微镜广泛用于研究各种材料的晶界和杂质等缺陷。然而，这需要大量的图像才能提取出准确的统计信息，同时还需手动完成，既耗时又会因人而异导致结果不一致。现在，美国威斯康星大学和橡树岭国家实验室的研究团队将机器学习、计算机视觉和图像分析技术相结合，获取有关缺陷尺寸和缺陷类型的信息。在分析质量方面，该程序的工作效果已经与人工的相当。若进一步改进，此法将可对大数据集作实时分析。该文近期发表于npj Computational Materials 4:36 (2018)。
Editorial Summary原文
Electron microcopy: Speeding things up
An automatic tool is able to identify defects from several electron microscopy images. Electron microscopy is widely used to study defects like grain boundaries and impurities in a wide range of materials. However, a large number of images are needed to extract statistically significant information, while identification is still done manually which is not only time-consuming but also inconsistent, depending on the identifier. Now, a team from University of Wisconsin and Oak Ridge National Laboratory in the USA combine machine learning, computer vision, and image analysis techniques to obtain information about the defects size and type. The program’s performance is comparable to manual analysis in terms of quality. Further improvement can lead to real-time analysis from large data sets.
原文Abstract及其翻译
Automated defect analysis in electron microscopic images (基于电子显微图像的缺陷自动分析)
Wei Li, Kevin G. Field & Dane Morgan
Abstract Electron microscopy and defect analysis are a cornerstone of materials science, as they offer detailed insights on the microstructure and performance of a wide range of materials and material systems. Building a robust and flexible platform for automated defect recognition and classification in electron microscopy will result in the completion of analysis orders of magnitude faster after images are recorded, or even online during image acquisition. Automated analysis has the potential to be significantly more efficient, accurate, and repeatable than human analysis, and it can scale with the increasingly important methods of automated data generation. Herein, an automated recognition tool is developed based on a computer vison–based approach; it sequentially applies a cascade object detector, convolutional neural network, and local image analysis methods. We demonstrate that the automated tool performs as well as or better than manual human detection in terms of recall and precision and achieves quantitative image/defect analysis metrics close to the human average. The proposed approach works for images of varying contrast, brightness, and magnification. These promising results suggest that this and similar approaches are worth exploring for detecting multiple defect types and have the potential to locate, classify, and measure quantitative features for a range of defect types, materials, and electron microscopic techniques.
摘要 电子显微镜和缺陷分析作为材料科学的基石，可为各类材料体系提供微观结构和性能的详细信息。为电子显微镜创建一个强大而灵活的缺陷自动识别和分类平台，将可在图像记录后甚至在图像采集过程中更快地完成分析任务。与人工分析相比，自动分析有望显著提高分析的效率、准确性和可重复性，并可通过日渐显要的自动数据生成方法进行扩展。本研究基于计算机视觉方法，开发了一种自动识别工具;依次应用了级联对象检测器、卷积神经网络和局部图像分析方法。该自动化工具已被证明在回溯和精确度方面与人工手动检测水平相当或更好，在图像/缺陷分析定量指标方面，接近人类平均水平。此方法适用于不同对比度、不同亮度和不同放大倍数的图像。该研究结果表明，此法或类似方法值得深入探索，以便能检测多种类型的缺陷，使其具有定位、分类、定量测量一系列类型的缺陷、材料和电子显微技术的特征。
