BirDrone: a novel dataset for enhanced drone and bird detection using YOLOv9

Muhamad Zamri, Fatin Najihah and Gunawan, Teddy Surya and Yusoff, Siti Hajar and Mohd. Mustafah, Yasir and Kartiwi, Mira and Md Yusoff, Nelidya (2024) BirDrone: a novel dataset for enhanced drone and bird detection using YOLOv9. In: IEEE 10th International Conference on Smart Instrumentation, Measurement and Applications, 30-31 July 2024, Bandung, Indonesia.

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Abstract

Drones present substantial detection challenges due to their capacity to operate in various conditions, including low lighting, harsh weather, and similar objects like birds. Existing datasets frequently fail to address all of these challenges comprehensively. The BirDrone dataset, specifically designed to improve the accuracy of distinguishing between drones and birds, is introduced to address this issue, with a particular emphasis on small-scale objects. The dataset comprises images with intricate backgrounds and lighting conditions to enhance detection reliability. By utilizing the YOLOv9 model to assess the dataset, we achieved a high level of accuracy and significantly reduced the number of false alarms. The BirDrone dataset's development process, data augmentation methodologies, and performance outcomes of YOLOv9 are all detailed in this paper, which serves as a testament to its efficacy in practical applications.

Item Type:	Proceeding Paper (Slide Presentation)
Uncontrolled Keywords:	drone detection, BirDrone dataset, YOLOv9, object detection, deep learning.
Subjects:	T Technology > T Technology (General) T Technology > T Technology (General) > T10.5 Communication of technical information
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering > Department of Electrical and Computer Engineering Kulliyyah of Engineering
Depositing User:	dr siti hajar yusoff
Date Deposited:	22 Oct 2024 14:44
Last Modified:	22 Oct 2024 14:44
URI:	http://irep.iium.edu.my/id/eprint/115182

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