Robust real-time aircraft detection with multi-task cascaded calibration networks

Aircraft detection is notoriously challenging owing to the orientation and size variations of aircraft objects. Existing detection pipelines compromise with efficiency or accuracy to deal with the large visual variations. We present a novel cascaded framework that joins object detection and orientation prediction through multi-task learning. The cascaded framework consists of three stages and operates in a coarse-to-fine manner. Each stage simultaneously rejects false targets, regresses the locations of object candidates, and calibrates the orientations of the candidates to upright gradually.