Drone model identification by convolutional neural network from video stream

Simple item page
dc.contributor.authorWisniewski, Mariusz
dc.contributor.authorRana, Zeeshan A.
dc.contributor.authorPetrunin, Ivan
dc.date.accessioned2022-01-27T11:45:02Z
dc.date.available2022-01-27T11:45:02Z
dc.date.issued2021-11-15
dc.description.abstractWe present a convolutional neural network model that correctly identifies drone models in real-life video streams of flying drones. To achieve this, we show a method of generating synthetic drone images. To create a diverse dataset, the simulation parameters (such as drone textures, lighting, and orientation) are randomized. This synthetic dataset is used to train a convolutional neural network to identify the drone model: DJI Phantom, DJI Mavic, or DJI Inspire. The model is then tested on a real-life Anti-UAV dataset of flying drones. The benchmark results show that the DenseNet201 architecture performed the best. Adding Gaussian noise to the training dataset and performing full training (as opposed to freezing layers) shows the best results. The model shows an average accuracy of 92.4%, and an average precision of 88.6% on the test dataset.en_UK
dc.identifier.citationWisniewski M, Rana ZA, Petrunin I. (2021) Drone model identification by convolutional neural network from video stream. In: 2021 AIAA/IEEE 40th Digital Avionics Systems Conference (DASC), 3-7 October 2021, San Antonio, TX, USAen_UK
dc.identifier.eisbn978-1-6654-3420-1
dc.identifier.isbn978-1-6654-3421-8
dc.identifier.issn2155-7209
dc.identifier.urihttps://doi.org/10.1109/DASC52595.2021.9594392
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/17506
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectUnmanned Aerial Vehiclesen_UK
dc.subjectdronesen_UK
dc.subjectairport securityen_UK
dc.subjectconvolutional neural networken_UK
dc.subjectanti-uaven_UK
dc.subjectsynthetic imagesen_UK
dc.subjectdomain randomizationen_UK
dc.subjectsynthetic dronesen_UK
dc.titleDrone model identification by convolutional neural network from video streamen_UK
dc.typeConference paperen_UK

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Drone_model_identification-2021.pdf
Size:
676.95 KB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.63 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Staff publications (SATM)