Drone model classification using convolutional neural network trained on synthetic data

Simple item page
dc.contributor.authorWisniewski, Mariusz
dc.contributor.authorRana, Zeeshan A.
dc.contributor.authorPetrunin, Ivan
dc.date.accessioned2022-08-23T12:34:59Z
dc.date.available2022-08-23T12:34:59Z
dc.date.issued2022-08-12
dc.description.abstractWe present a convolutional neural network (CNN) that identifies drone models in real-life videos. The neural network is trained on synthetic images and tested on a real-life dataset of drone videos. To create the training and validation datasets, we show a method of generating synthetic drone images. Domain randomization is used to vary the simulation parameters such as model textures, background images, and orientation. Three common drone models are classified: DJI Phantom, DJI Mavic, and DJI Inspire. To test the performance of the neural network model, Anti-UAV, a real-life dataset of flying drones is used. The proposed method reduces the time-cost associated with manually labelling drones, and we prove that it is transferable to real-life videos. The CNN achieves an overall accuracy of 92.4%, a precision of 88.8%, a recall of 88.6%, and an f1 score of 88.7% when tested on the real-life dataset.en_UK
dc.identifier.citationWisniewski M, Rana ZA, Petrunin I. (2022) Drone model classification using convolutional neural network trained on synthetic data. Journal of Imaging, Volume 8, Issue 8, August 2022, Article number 218en_UK
dc.identifier.issn2313-433X
dc.identifier.urihttps://doi.org/10.3390/jimaging8080218
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/18350
dc.language.isoenen_UK
dc.publisherMDPIen_UK
dc.relation.isreferencedbyhttps://dspace.lib.cranfield.ac.uk/handle/1826/23082
dc.relation.isreferencedbyhttps://doi.org/10.17862/cranfield.rd.19423925
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectunmanned aerial vehiclesen_UK
dc.subjectdronesen_UK
dc.subjectairport securityen_UK
dc.subjectconvolutional neural networken_UK
dc.subjectsynthetic imagesen_UK
dc.subjectsynthetic dataen_UK
dc.subjectdomain randomizationen_UK
dc.subjectdrone detectionen_UK
dc.subjectdrone classificationen_UK
dc.subjectdrone identificationen_UK
dc.subjectartificial intelligenceen_UK
dc.titleDrone model classification using convolutional neural network trained on synthetic dataen_UK
dc.typeArticleen_UK

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Drone_model_classification-2022.pdf
Size:
4.9 MB
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)