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Introducing AOD 4: A dataset for air borne object detection.

Vama Soni1, Dhruval Shah2, Jeel Joshi2

  • 1Department 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.

Data in Brief
|September 5, 2024
PubMed
Summary
This summary is machine-generated.

A new airborne object dataset with 22,516 images of airplanes, helicopters, drones, and birds is introduced. This dataset aids in developing advanced object detection and tracking algorithms for public safety and security applications.

Keywords:
AirplanesBirdComplex environmentDronesHelicopter

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Data Science

Background:

  • Accurate detection and tracking of airborne objects are crucial for various security and safety applications.
  • Existing datasets may lack diversity or sufficient scale for robust algorithm development.

Purpose of the Study:

  • To introduce a comprehensive airborne object dataset for research and development.
  • To facilitate the creation of improved algorithms for airborne object detection and tracking.

Main Methods:

  • Compilation of 22,516 images from YouTube-8 M, Anti-UAV, and Ahmed Mohsen's dataset.
  • Conversion of video data into individual frames.
  • Annotation of images into four classes: airplanes, helicopters, drones, and birds using Roboflow's tool, yielding 7,900 annotations per class.

Main Results:

  • A large-scale, annotated dataset of airborne objects is now available.
  • The dataset contains 22,516 images across four distinct categories.
  • Each class features 7,900 annotations, ensuring balanced representation.

Conclusions:

  • The introduced dataset provides a valuable resource for advancing airborne object recognition technologies.
  • It supports the development of algorithms for applications in military surveillance, border security, and public safety.
  • Further research can leverage this dataset to enhance the performance and reliability of automated airborne object detection systems.