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

• Main Authors: Muhamad Zamri, Fatin Najihah, Gunawan, Teddy Surya, Yusoff, Siti Hajar, Mohd. Mustafah, Yasir, Kartiwi, Mira, Md Yusoff, Nelidya
• Format: Proceeding Paper
• Language: English
• Published: IEEE 2024
• Subjects: T Technology (General); T10.5 Communication of technical information
• Online Access: http://irep.iium.edu.my/115182/7/115182_BirDrone%20a%20novel.pdf; http://irep.iium.edu.my/115182/; https://ieeexplore.ieee.org/document/10675527; https://doi.org/10.1109/ICSIMA62563.2024.10675527
• Institution: Universiti Islam Antarabangsa Malaysia

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...