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.

翻译：晶体缺陷（如线状位错）对金属器件的性能与可靠性具有重要影响。其相互作用与演化过程仍为材料科学与材料物理学提出诸多未解问题。原位透射电镜实验可为位错行为与运动机制提供重要见解。此类实验过程中，位错微结构以视频形式被捕获。单帧图像分析虽能提供有价值信息，但受限于自动化识别、数字化及定量提取曲线状位错目标的能力。海量数据亦使人工标注极为耗时，从而限制了基于深度学习的位错微结构自动图像分析与分割技术应用。本研究开发了一种用于生成位错分割训练数据的参数化合成模型。尽管领域专家可能认为合成训练图像过于人工化，但我们的研究结果表明，此类数据可带来更优性能，尤其能提升深度学习模型对不同微结构及成像条件的泛化能力。此外，我们提出了一种针对重叠或交叉位错线分割的增强型深度学习方法。通过四个真实数据集对该框架进行测试发现，合成训练数据在真实图像上同样能获得高质量结果——若再结合少量真实图像进行微调，效果则更为显著。