Earticle

초록

영어

The use of drones is rapidly increasing in sports, photography, and entertainment purposes because of their affordable price and lightweight nature. However, this potential increment in the of drone of drone is creating safety and security threats. The detection of drones is necessary to overcome these issues. The detection of drones may be challenging because of the presence of other aerial objects like aircraft and birds. The existing systems used for drone detection employed a small dataset with a lack of diverse images. To overcome the limitation in previous studies, in this study, we used a largescale dataset drone images dataset. We conducted experiments on different You Only Look Once Version 8 (Yolov8) models using this dataset. All the trained models are evaluated in terms of precision, recall, mAP50, and mAP50-95. Yolov8x exhibits high performance in terms of precision, recall, mAP50, mAP50-95 models among other models which shows the superiority of Yolov8x in drone detection technology.

목차

Abstract
1. Introduction
2. Methods
2.1. Dataset
2.2. Experiment setup
3. Experiment result
4. Conclusions
Acknowledgment
References

저자

• Arailym Dosset [ Department of Computer Science & Engineering Sejong Univerity ]
• Sufyan Danish [ Department of Computer Science & Engineering Sejong Univerity ]
• Fayaz Muhammad [ Department of Computer Science & Engineering Sejong Univerity ]
• L. Minh Dang [ Department of Information and Communication Engineering and Convergence Engineering for Intelligent Drone Sejong Univerity ]
• Hyeonjoon Moon [ Department of Computer Science & Engineering Sejong Univerity ]

참고문헌