Archangel Dataset: UAV-based Imagery with Position and Pose Metadata

Object detection on imagery captured onboard aerial platforms involves different challenges than in ground-to-ground object detection. For example, images captured from UAVs with varying altitude and view angles present challenges for machine learning that are due to variations in appearance and scene attributes. Thus, it is essential to closely examine the critical variables that impact object detection from UAV platforms, such as the significant variations in pose, range to objects, background clutter, lighting, weather conditions, and velocity/acceleration of the UAV. To that end, in this work, we introduce a UAV-based image dataset, called the Archangel dataset, collected with a UAV that includes pose and range information in the form of metadata. Additionally, we use the Archangel dataset to conduct comprehensive studies of how the critical attributes of UAV-based images affect machine learning models for object detection. The extensive analysis on the Archangel dataset aims to advance optimal training and testing of machine learning models in general as well as the more specific case of UAV-based object detection using deep neural networks.