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http://hdl.handle.net/2080/4096
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DC Field | Value | Language |
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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 |
Files in This Item:
File | Description | Size | Format | |
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2023_TENCON23_UAV_SriLakshmi.pdf | 585.53 kB | Adobe PDF | View/Open Request a copy |
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