  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.
