Soni Vama, Shah Dhruval, Joshi Jeel, Gite Shilpa, Pradhan Biswajeet, Alamri Abdullah
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.
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.
Data Brief. 2024 Aug 6;56:110801. doi: 10.1016/j.dib.2024.110801. eCollection 2024 Oct.
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.
本文介绍了一个机载物体数据集，该数据集包含22516张图像，对四类机载物体进行了分类：飞机、直升机、无人机和鸟类。该数据集是从YouTube-8M、反无人机数据集以及托管在Roboflow上的艾哈迈德·穆罕默德数据集编译而来的。视频来自前两个平台并转换为单帧，而后一个数据集已经由图像组成。收集之后，该数据集利用Roboflow的注释工具进行了标记和注释处理，每个类别产生了7900个注释。研究人员可以利用这个数据集来开发和完善用于机载物体检测和跟踪的算法，其潜在应用涵盖军事监视、边境安全和公共安全等领域。
