BirdsEyeView: Aerial View Dataset for Object Classification and Detection
BirdsEyeView: Aerial View Dataset for Object Classification and Detection
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DOI：
10.1109/gcwkshps45667.2019.9024557
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发表时间：
2019-12
期刊：
影响因子：
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通讯作者：
Yun-long Qi;Dong Wang;Junfei Xie;K. Lu;Yan Wan;Shengli Fu
中科院分区：
文献类型：
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作者：
Yun-long Qi;Dong Wang;Junfei Xie;K. Lu;Yan Wan;Shengli Fu
近年来，由于计算能力和高质量数据集的显着增加，基于深度学习的计算机视觉技术发展迅速。在这篇文章中，我们提出了一个鸟瞰图像和视频数据集，致力于促进无人机平台上的视觉应用，如目标检测，分类和跟踪。该数据集由5，000张图像组成，每一张都根据PASCAL VOC的指导方针进行了仔细的注释。该数据集旨在覆盖不同的现实生活场景，具有不同于其他数据集的空中视角。这种特定的数据集对于开发和测试无人机应用的深度学习算法非常重要。此外，该数据集可以作为评估无人机视觉解决方案的基准。
In recent years, deep learning based computer vision technology has progressed rapidly thanks to the significant increases in computing power and high-quality datasets. In this article, we present an aerial view image and video dataset dedicated to facilitating vision applications on the UAV platform, such as object detection, classification and tracking. The dataset consists of 5,000 images, each of which is carefully annotated according to the guidelines of the PASCAL VOC. The dataset is designed to cover diverse real-life scenes with aerial view angles which is different from other datasets. Such kind of specific dataset will be of great importance in developing and testing deep learning algorithms for UAV applications. Moreover, the dataset can serve as a benchmark to evaluate UAV visual solutions.