Detecting small objects in video streams of head-worn augmented reality devices in near real-time is a huge challenge: training data is typically scarce, the input video stream can be of limited quality, and small objects are notoriously hard to detect. In industrial scenarios, however, it is often possible to leverage contextual knowledge for the detection of small objects. Furthermore, CAD data of objects are typically available and can be used to generate synthetic training data. We describe a near real-time small object detection pipeline for egocentric perception in a manual assembly scenario: We generate a training data set based on CAD data and realistic backgrounds in Unity. We then train a YOLOv4 model for a two-stage detection process: First, the context is recognized, then the small object of interest is detected. We evaluate our pipeline on the augmented reality device Microsoft Hololens 2.

中文翻译：

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手动装配场景中近实时以自我为中心的小物体检测

近乎实时地检测头戴式增强现实设备视频流中的小物体是一个巨大的挑战：训练数据通常很少，输入视频流的质量可能有限，而且小物体是出了名的难以检测。然而，在工业场景中，通常可以利用上下文知识来检测小物体。此外，对象的 CAD 数据通常是可用的，可用于生成合成训练数据。我们描述了在手动装配场景中用于以自我为中心的感知的近实时小物体检测管道：我们基于 CAD 数据和 Unity 中的现实背景生成训练数据集。然后我们为两阶段检测过程训练 YOLOv4 模型：首先，识别上下文，然后检测感兴趣的小对象。