  The growing ubiquity of drones has raised concerns over the ability of
traditional air-space monitoring technologies to accurately characterise such
vehicles. Here, we present a CNN using a decision tree and ensemble structure
to fully characterise drones in flight. Our system determines the drone type,
orientation (in terms of pitch, roll, and yaw), and performs segmentation to
classify different body parts (engines, body, and camera). We also provide a
computer model for the rapid generation of large quantities of accurately
labelled photo-realistic training data and demonstrate that this data is of
sufficient fidelity to allow the system to accurately characterise real drones
in flight. Our network will provide a valuable tool in the image processing
chain where it may build upon existing drone detection technologies to provide
complete drone characterisation over wide areas.
