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

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 t...

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Bibliographic Details
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
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Summary: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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2022.3162866