Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/4096
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dc.contributor.authorSri Lakshmi, N V V N J-
dc.contributor.authorGhosh, Shrabani-
dc.contributor.authorDas, Santos Kumar-
dc.date.accessioned2023-11-17T11:19:41Z-
dc.date.available2023-11-17T11:19:41Z-
dc.date.issued2023-10-
dc.identifier.citationIEEE Region 10 Technical Conference 2023, Chiang Mai, Thailand, 31 October - 3 November 2023en_US
dc.identifier.urihttp://hdl.handle.net/2080/4096-
dc.descriptionCopyright belongs to proceeding publisheren_US
dc.description.abstractEvery 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%, respectivelyen_US
dc.subjectDeep Learningen_US
dc.subjectUAV Detectionen_US
dc.titleUAV Intrusion Detection using Deep Learning Approachesen_US
dc.typeArticleen_US
Appears in Collections:Conference Papers

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