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.

电子显微镜和缺陷分析是材料科学的基石，因为它们提供了有关各种材料和材料系统的微观结构和性能的详细见解。为电子显微镜中的缺陷自动识别和分类建立一个强大而灵活的平台，将导致在记录图像后甚至在图像采集过程中在线完成分析数量级的速度更快。与人工分析相比，自动化分析具有显着提高效率，准确性和可重复性的潜力，并且可以随着越来越重要的自动化数据生成方法进行扩展。在此，基于基于计算机视觉的方法开发了一种自动识别工具。它顺序地应用了级联目标检测器，卷积神经网络，和本地图像分析方法。我们证明，在召回率和精确度方面，自动化工具的性能优于或优于人工人工检测，并且可以实现接近人类平均水平的定量图像/缺陷分析指标。所提出的方法适用于对比度，亮度和放大率不同的图像。这些有前途的结果表明，这种方法和类似方法值得探索以检测多种缺陷类型，并且有可能对一系列缺陷类型，材料和电子显微镜技术进行定位，分类和测量定量特征。所提出的方法适用于对比度，亮度和放大率不同的图像。这些有前途的结果表明，这种方法和类似方法值得探索以检测多种缺陷类型，并且有可能对一系列缺陷类型，材料和电子显微镜技术进行定位，分类和测量定量特征。所提出的方法适用于对比度，亮度和放大率不同的图像。这些有前途的结果表明，这种方法和类似方法值得探索以检测多种缺陷类型，并且有可能对一系列缺陷类型，材料和电子显微镜技术进行定位，分类和测量定量特征。