  Aerial pixel-wise scene perception of the surrounding environment is an
important task for UAVs (Unmanned Aerial Vehicles). Previous research works
mainly adopt conventional pinhole cameras or fisheye cameras as the imaging
device. However, these imaging systems cannot achieve large Field of View
(FoV), small size, and lightweight at the same time. To this end, we design a
UAV system with a Panoramic Annular Lens (PAL), which has the characteristics
of small size, low weight, and a 360-degree annular FoV. A lightweight
panoramic annular semantic segmentation neural network model is designed to
achieve high-accuracy and real-time scene parsing. In addition, we present the
first drone-perspective panoramic scene segmentation dataset Aerial-PASS, with
annotated labels of track, field, and others. A comprehensive variety of
experiments shows that the designed system performs satisfactorily in aerial
panoramic scene parsing. In particular, our proposed model strikes an excellent
trade-off between segmentation performance and inference speed suitable,
validated on both public street-scene and our established aerial-scene
datasets.
