DroneSense: The Identification, Segmentation, and Orientation Detection of Drones via Neural Networks

• Published in: IEEE access 2022, Vol.10, p.38154-38164
• Main Authors: Scholes, Stirling, Ruget, Alice, Mora-Martin, German, Zhu, Feng, Gyongy, Istvan, Leach, Jonathan
• Format: Article
• Language: English
• Subjects: Air monitoring; Airspace; Body parts; Cameras; Convolutional neural network; Decision trees; Drones; Engines; Feature extraction; Image processing; Image segmentation; Laser radar; Optical imaging; orientation detection; Pitch (inclination); pose; Rolling motion; segmentation; Yaw
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2022.3162866

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.