UAV Intrusion Detection using Deep Learning Approaches

Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/4096

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DC Field	Value	Language
dc.contributor.author	Sri Lakshmi, N V V N J	-
dc.contributor.author	Ghosh, Shrabani	-
dc.contributor.author	Das, Santos Kumar	-
dc.date.accessioned	2023-11-17T11:19:41Z	-
dc.date.available	2023-11-17T11:19:41Z	-
dc.date.issued	2023-10	-
dc.identifier.citation	IEEE Region 10 Technical Conference 2023, Chiang Mai, Thailand, 31 October - 3 November 2023	en_US
dc.identifier.uri	http://hdl.handle.net/2080/4096	-
dc.description	Copyright belongs to proceeding publisher	en_US
dc.description.abstract	Every day, there is an upsurge in the number of terrorist attacks carried out by drones. As a result, drone detection has become mandatory. Real-time detection of drones is a very challenging task due to their small size, lightning conditions, and relative viewing angles. In this article, a new UAV dataset is presented to perform drone detection tasks using two deep learning techniques, YOLOv5 and YOLOv8, along with the existing Det-Fly dataset. Implementing the YOLOv5 technique, the mean average precision (mAP) for drone detection on both the Det-Fly and UAV datasets is 97.2% and 94.1%, respectively. Similarly, the corresponding values for the YOLOv8 algorithm are 99.5% and 95.0%, respectively	en_US
dc.subject	Deep Learning	en_US
dc.subject	UAV Detection	en_US
dc.title	UAV Intrusion Detection using Deep Learning Approaches	en_US
dc.type	Article	en_US
Appears in Collections:	Conference Papers

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