Publication Title
Drones
Document Type
Article
Abstract/Description
Drones are increasing in popularity and are reaching the public faster than ever before. Consequently, the chances of a drone being misused are multiplying. Automated drone detection is necessary to prevent unauthorized and unwanted drone interventions. In this research, we designed an automated drone detection system using YOLOv4. The model was trained using drone and bird datasets. We then evaluated the trained YOLOv4 model on the testing dataset, using mean average precision (mAP), frames per second (FPS), precision, recall, and F1-score as evaluation parameters. We next collected our own two types of drone videos, performed drone detections, and calculated the FPS to identify the speed of detection at three altitudes. Our methodology showed better performance than what has been found in previous similar studies, achieving a mAP of 74.36%, precision of 0.95, recall of 0.68, and F1-score of 0.79. For video detection, we achieved an FPS of 20.5 on the DJI Phantom III and an FPS of 19.0 on the DJI Mavic Pro.
Department
Engineering and Technology
First Page
95
Last Page
115
DOI
10.3390/drones5030095
Volume
5
Issue
3
ISSN
2504-446X
Date
9-11-2021
Citation Information
Singha, Subroto and Aydin, Burchan, "Automated Drone Detection Using YOLOv4" (2021). Faculty Publications. 200.
https://digitalcommons.tamuc.edu/cose-faculty-publications/200
