Deploying deep learning-based applications in specialized domains like the aircraft production industry typically suffers from the training data availability problem. Only a few datasets represent non-everyday objects, situations, and tasks. Recent advantages in research around Vision Foundation Models (VFM) opened a new area of tasks and models with high generalization capabilities in non-semantic and semantic predictions. As recently demonstrated by the Segment Anything Project, exploiting VFM's zero-shot capabilities is a promising direction in tackling the boundaries spanned by data, context, and sensor variety. Although, investigating its application within specific domains is subject to ongoing research. This paper contributes here by surveying applications of the SAM in aircraft production-specific use cases. We include manufacturing, intralogistics, as well as maintenance, repair, and overhaul processes, also representing a variety of other neighboring industrial domains. Besides presenting the various use cases, we further discuss the injection of domain knowledge.

翻译：在航空制造等专业领域部署基于深度学习的应用通常面临训练数据可用性问题。仅有少数数据集能代表非日常物体、场景及任务。近期视觉基础模型（Vision Foundation Models, VFMs）研究的进展开辟了在非语义和语义预测中具备高泛化能力的任务与模型新领域。正如分割一切项目（Segment Anything Project）近期所展示的，利用VFM的零样本能力是应对数据、情境与传感器多样性所带来边界问题的有前景方向。然而，其在特定领域中的应用研究仍在进行中。本文通过调研SAM在航空制造特定用例中的应用为此做出贡献。我们涵盖了制造、内部物流以及维护、修理和大修流程，这些同样代表了其他相邻工业领域。除展示各类用例之外，我们进一步探讨了领域知识的注入。