SPARK: SPAcecraft Recognition leveraging Knowledge of Space Environment

This paper proposes the SPARK dataset as a new unique space object multi-modal image dataset. Image-based object recognition is an important component of Space Situational Awareness, especially for applications such as on-orbit servicing, active debris removal, and satellite formation. However, the...

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Bibliographic Details
Published in:arXiv.org 2021-04
Main Authors: Mohamed Adel Musallam, Kassem Al Ismaeil, Oyedotun, Oyebade, Marcos Damian Perez, Poucet, Michel, Aouada, Djamila
Format: Article
Language:English
Subjects:
Active satellites
Aerospace environments
Algorithms
Datasets
Object recognition
Orbital servicing
Situational awareness
Spacecraft
Online Access:Get full text
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Summary:This paper proposes the SPARK dataset as a new unique space object multi-modal image dataset. Image-based object recognition is an important component of Space Situational Awareness, especially for applications such as on-orbit servicing, active debris removal, and satellite formation. However, the lack of sufficient annotated space data has limited research efforts in developing data-driven spacecraft recognition approaches. The SPARK dataset has been generated under a realistic space simulation environment, with a large diversity in sensing conditions for different orbital scenarios. It provides about 150k images per modality, RGB and depth, and 11 classes for spacecrafts and debris. This dataset offers an opportunity to benchmark and further develop object recognition, classification and detection algorithms, as well as multi-modal RGB-Depth approaches under space sensing conditions. Preliminary experimental evaluation validates the relevance of the data, and highlights interesting challenging scenarios specific to the space environment.
ISSN:2331-8422