Drone Detection Using YOLOv5

Author(s)/Creator(s)

Burchan Aydin, Texas A & M University - CommerceFollow
Subroto Singha, Texas A & M University - CommerceFollow

Publication Title

Eng

Document Type

Article

Abstract/Description

The rapidly increasing number of drones in the national airspace, including those for recreational and commercial applications, has raised concerns regarding misuse. Autonomous drone detection systems offer a probable solution to overcoming the issue of potential drone misuse, such as drug smuggling, violating people’s privacy, etc. Detecting drones can be difficult, due to similar objects in the sky, such as airplanes and birds. In addition, automated drone detection systems need to be trained with ample amounts of data to provide high accuracy. Real-time detection is also necessary, but this requires highly configured devices such as a graphical processing unit (GPU). The present study sought to overcome these challenges by proposing a one-shot detector called You Only Look Once version 5 (YOLOv5), which can train the proposed model using pre-trained weights and data augmentation. The trained model was evaluated using mean average precision (mAP) and recall measures. The model achieved a 90.40% mAP, a 21.57% improvement over our previous model that used You Only Look Once version 4 (YOLOv4) and was tested on the same dataset.

Department

Engineering and Technology

Department

Computer Science and Information Systems

First Page

416

Last Page

433

DOI

https://doi.org/10.3390/eng4010025

Volume

4

Issue

25

ISSN

2673-4117

Date

2-2023

Publisher

MDPI AG

Comments

Drone Detection Using YOLOv5 published in Eng, 4, no. 29, (February 2023), under a Creative Commons Attribution License.

Citation Information

Aydin, Burchan and Singha, Subroto, "Drone Detection Using YOLOv5" (2023). Faculty Publications. 7.
https://digitalcommons.tamuc.edu/cose-faculty-publications/7