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Amirzdeh, M., Hosseini Moradi, S., ghobadi, N. (2023). Real Time Detection of Multi-Rotor Unmanned Aerial Vehicle Using YOLOv5 Optimized Algorithm. Electronic and Cyber Defense, 14(1), 11-22.
Majid Amirzdeh; Seyed Ali Hosseini Moradi; Nader ghobadi. "Real Time Detection of Multi-Rotor Unmanned Aerial Vehicle Using YOLOv5 Optimized Algorithm". Electronic and Cyber Defense, 14, 1, 2023, 11-22.
Amirzdeh, M., Hosseini Moradi, S., ghobadi, N. (2023). 'Real Time Detection of Multi-Rotor Unmanned Aerial Vehicle Using YOLOv5 Optimized Algorithm', Electronic and Cyber Defense, 14(1), pp. 11-22.
Amirzdeh, M., Hosseini Moradi, S., ghobadi, N. Real Time Detection of Multi-Rotor Unmanned Aerial Vehicle Using YOLOv5 Optimized Algorithm. Electronic and Cyber Defense, 2023; 14(1): 11-22.

Real Time Detection of Multi-Rotor Unmanned Aerial Vehicle Using YOLOv5 Optimized Algorithm

علوم و فناوریهای پدافند نوین
Article 2, Volume 14, Issue 1 - Serial Number 51, September 2023, Pages 11-22    PDF (1.01 M)
Document Type: -
Authors
Majid Amirzdeh1; Seyed Ali Hosseini Moradi* 2; Nader ghobadi3
1PhD student, Hamedan University, Hamedan, Iran
2Researcher, Khatam Al Anbia Air Defense University, Tehran, Iran
3Associate Professor, Malayer University, Malayer, Iran
Receive Date: 09 March 2023,  Revise Date: 04 April 2022,  Accept Date: 20 April 2023 
Abstract
In recent years, the very rapid development of technology in the field of UAVs (Unmanned Aerial Vehicles), along with its advantages, has brought serious threats at various social and security levels. Among these problems, the issue of unauthorized flights in protected and security area can be mentioned. Therefore, real time detection of these devices is necessary in order to quickly carry out relevant measures. In this regard, in this research, using the YOLOv5l algorithm, which is part of the latest version of one-stage computer vision algorithms, two models with SGD and Adam optimizers have been developed for the real time detection of UAVs. To develop the models in this research, a dataset containing 10046 images of different types and states of UAVs has been used. The processing of the models has been done with the Google Colab platform, which provides a free powerful processing system for developers. The models have been evaluated on four test sets of 1000 images including normal, low volume, night mode, and gray scale and also a test set including 100 images from several UAVs. According to the results, the developed model with The Adam optimizer performed better than the model developed with the SGD optimizer.
Keywords
UAV (Unmanned Aerial Vehicle); Real Time Detection; Multi-Rotor UAV; YOLOv5
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