High-performance panoramic annular lens design for real-time semantic segmentation on aerial imagery

As the visual perception window of the drone system, the lens provides great help for obtaining visual information, detection, and recognition. However, traditional lenses carried on drones cannot have characteristics of a large field of view (FoV), small size, and low weight at the same time. To meet the above requirements, we propose a panoramic annular lens (PAL) system with 4K high resolution, a large FoV of (30 deg to 100 deg) × 360 deg, an angular resolution of 12.2 mrad of aerial perspective, and great imaging performance. We equip a drone system with our designed PAL to collect panoramic image data at an altitude of 100 m from the track and field and obtain the first drone-perspective panoramic scene segmentation dataset Aerial-PASS, with annotated labels of track and field. We design an efficient deep architecture for aerial scene segmentation. Trained on Aerial-PASS, the yielded model accurately segments aerial images. Compared with the ERF-PAPNet and SwiftNet semantic segmentation networks, the network we adopted has higher recognition accuracy with the mean IoU greater than 86.30%, which provides an important reference for the drone system to monitor and identify specific targets in real-time in a large FoV.