Article
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YOLOv8 Based Drone Detection: Performance Analysis and Optimization
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Version 1
: Received: 25 July 2024 / Approved: 26 July 2024 / Online: 26 July 2024 (05:24:18 CEST)
How to cite: Yilmaz, B.; Kutbay, U. YOLOv8 Based Drone Detection: Performance Analysis and Optimization. Preprints 2024, 2024072127. https://doi.org/10.20944/preprints202407.2127.v1 Yilmaz, B.; Kutbay, U. YOLOv8 Based Drone Detection: Performance Analysis and Optimization. Preprints 2024, 2024072127. https://doi.org/10.20944/preprints202407.2127.v1
Abstract
The extensive utilization of drones has led to numerous scenarios that encompass both advantageous and perilous outcomes. By using deep learning techniques, it is aimed to reduce the dangerous effects of drone use through early detection of drones.The state of the art of the study is evaluation of deep learning approaches such as pre trained YOLOv8 drone detection for security issues. In this study, dataset collected by Mehdi Özel for a UAV competition are sourced from GitHub. These images are labeled using Roboflow, and the model is trained on Google Colab. To enhance model performance, dataset augmentation techniques including rotation and blurring are implemented. Following these steps, a significant precision value of 0.946, a notable recall value of 0.9605 and a considerable precision-recall curve value of 0.978 are achieved, surpassing many popular models such as Mask CNN, CNN, and YOLOv5.
Keywords
YOLOv8; drone detection; deep learning
Subject
Engineering, Electrical and Electronic Engineering
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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