AI-Driven Collaborative Satellite Object Detection for Space Sustainability
This addresses space sustainability by improving detection for satellite operators, but it is incremental as it builds on existing deep learning methods with a new collaborative approach.
The paper tackles the problem of detecting space objects for collision avoidance in low-Earth orbit by proposing a satellite clustering framework for collaborative deep learning-based detection, achieving competitive accuracy with a low size, weight, and power footprint.
The growing density of satellites in low-Earth orbit (LEO) presents serious challenges to space sustainability, primarily due to the increased risk of in-orbit collisions. Traditional ground-based tracking systems are constrained by latency and coverage limitations, underscoring the need for onboard, vision-based space object detection (SOD) capabilities. In this paper, we propose a novel satellite clustering framework that enables the collaborative execution of deep learning (DL)-based SOD tasks across multiple satellites. To support this approach, we construct a high-fidelity dataset simulating imaging scenarios for clustered satellite formations. A distance-aware viewpoint selection strategy is introduced to optimize detection performance, and recent DL models are used for evaluation. Experimental results show that the clustering-based method achieves competitive detection accuracy compared to single-satellite and existing approaches, while maintaining a low size, weight, and power (SWaP) footprint. These findings underscore the potential of distributed, AI-enabled in-orbit systems to enhance space situational awareness and contribute to long-term space sustainability.