从TEM图像中深度学习晶体缺陷：解决“训练数据永远不够”问题的一种方案
Deep Learning of Crystalline Defects from TEM images: A Solution for the Problem of "Never Enough Training Data"
AI总结：
针对TEM位错图像训练数据不足，本文提出参数化合成数据生成模型及面向重叠位错线的增强分割方法，可在四个真实数据集上实现高质量泛化，少量真实图像微调后效果更佳。
摘要：
晶体缺陷（如线状位错）对许多金属器件的性能和可靠性起着重要作用。它们的相互作用和演化仍给材料科学与材料物理带来大量悬而未决的问题。原位TEM实验能够为理解位错的行为和运动提供重要洞见。在此类实验过程中，位错微观结构以视频形式被记录下来。分析单个视频帧可以提供有用的认识，但受到自动化识别、数字化以及将位错作为曲线对象进行定量提取等能力的限制。海量数据也使人工标注非常耗时，从而限制了基于深度学习的自动化图像分析和位错微观结构分割的应用。在本研究中，我们开发了一种参数化模型，用于生成位错分割的合成训练数据。尽管领域科学家有时可能认为合成训练图像过于人工化而不予重视，但我们的研究结果表明，它们能够带来优越性能，尤其是在深度学习模型针对不同微观结构和成像条件的泛化方面。此外，我们提出了一种增强型深度学习方法，针对分割重叠或相交的位错线进行了优化。在四个不同的真实数据集上测试该框架后，我们发现合成训练数据也能够在真实图像上产生高质量结果——如果在少量真实图像上进行微调，效果会更加显著。
英文摘要：
Crystalline defects, such as line-like dislocations, play an important role for the performance and reliability of many metallic devices. Their interaction and evolution still poses a multitude of open questions to materials science and materials physics. In-situ TEM experiments can provide important insights into how dislocations behave and move. During such experiments, the dislocation microstructure is captured in form of videos. The analysis of individual video frames can provide useful insights but is limited by the capabilities of automated identification, digitization, and quantitative extraction of the dislocations as curved objects. The vast amount of data also makes manual annotation very time consuming, thereby limiting the use of Deep Learning-based, automated image analysis and segmentation of the dislocation microstructure. In this work, a parametric model for generating synthetic training data for segmentation of dislocations is developed. Even though domain scientists might dismiss synthetic training images sometimes as too artificial, our findings show that they can result in superior performance, particularly regarding the generalizing of the Deep Learning models with respect to different microstructures and imaging conditions. Additionally, we propose an enhanced deep learning method optimized for segmenting overlapping or intersecting dislocation lines. Upon testing this framework on four distinct real datasets, we find that our synthetic training data are able to yield high-quality results also on real images-even more so if fine-tune on a few real images was done.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
