Introducing AOD 4: A dataset for air borne object detection

Vama Soni; Dhruval Shah; Jeel Joshi; Shilpa Gite; Biswajeet Pradhan; Abdullah Alamri

doi:10.1016/j.dib.2024.110801

Introducing AOD 4: A dataset for air borne object detection

Data Brief. 2024 Aug 6:56:110801. doi: 10.1016/j.dib.2024.110801. eCollection 2024 Oct.

Authors

Vama Soni¹, Dhruval Shah², Jeel Joshi², Shilpa Gite³, Biswajeet Pradhan⁴, Abdullah Alamri⁵

Affiliations

¹ Department of Computer Science and Engineering, Devang Patel Institute of Advance of Technology and Research (DEPSTAR), Charotar University of Science and Technology (CHARUSAT), Changa, Gujarat, 388421, India.
² Department of Information Technology, Devang Patel Institute of Advance of Technology and Research (DEPSTAR), Charotar University of Science and Technology (CHARUSAT), Changa, Gujarat, 388421, India.
³ Department of Artificial Intelligence and Machine Learning, Symbiosis Institute of Technology, Symbiosis Centre of Applied Artificial Intelligence (SCAAI), Symbiosis International (Deemed) University, Pune, 412115, India.
⁴ Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), School of Civil and Environmental Engineering, University of Technology Sydney, NSW 2007, Australia.
⁵ Department of Geology and Geophysics, College of Science, King Saud University, Riyadh, Saudi Arabia.

Abstract

This paper introduces an airborne object dataset comprising 22,516 images categorizing four classes of airborne objects: airplanes, helicopters, drones, and birds. The dataset was compiled from YouTube-8 M, Anti-UAV, and Ahmed Mohsen's dataset hosted on Roboflow. Videos were sourced from the first two platforms and converted into individual frames, whereas the latter dataset already consisted of images. Following collection, the dataset underwent labelling and annotation processes utilizing Roboflow's annotation tool, resulting in 7,900 annotations per class. Researchers can leverage this dataset to develop and refine algorithms for airborne object detection and tracking, with potential applications spanning military surveillance, border security, and public safety.

Keywords: Airplanes; Bird; Complex environment; Drones; Helicopter.