Poster + Paper
6 June 2022 Archangel dataset: UAV-based imagery with position and pose metadata
Author Affiliations +
Conference Poster
Abstract
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.
© (2022) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yaesop Lee, Eung-Joo Lee, Damon M. Conover, Yi-Ting Shen, Heesung Kwon, Shuvra S. Bhattacharyya, Jason Hill, Kenneth Evensen, and G. Jeremy Leong "Archangel dataset: UAV-based imagery with position and pose metadata", Proc. SPIE 12113, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications IV, 121131T (6 June 2022); https://doi.org/10.1117/12.2618636
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KEYWORDS
Unmanned aerial vehicles

Performance modeling

Target detection

Cameras

Machine learning

Analytical research

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