Paper Title
Drone Surveillance using YOLOv8 Object Detection
Article Identifiers
Authors
Dhruti Bhadoria
Keywords
Object Detection , deep learning , YOLOv8
Abstract
The use of airborne vehicles like drones has increased with technical improvement. Undoubtedly, drones are a fantastic addition to surveillance, weather monitoring, traffic monitoring, firefighting, and many other fields. However, there is a flip side to this: as drones become more widely used, the risk of them being mishandled rises. To stop uninvited and unwanted drone interventions, automated drone detection is required. In this work, we looked into how to identify and categorize drones in aerial pictures and videos using the YOLOv8 object detection technique. I utilized a dataset of several drone photos and videos for this, and I trained the model using that dataset.
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How To Cite (APA)
Dhruti Bhadoria (May-2023). Drone Surveillance using YOLOv8 Object Detection. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(5), i218-i223. https://ijnrd.org/papers/IJNRD2305829.pdf
Issue
Volume 8 Issue 5, May-2023
Pages : i218-i223
Other Publication Details
Paper Reg. ID: IJNRD_196469
Published Paper Id: IJNRD2305829
Downloads: 000121983
Research Area: Computer Science & TechnologyÂ
Country: Gwalior , Madhya Pradesh, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2305829.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2305829
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Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)
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This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition
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