AI-Driven Collaborative Satellite Object Detection for Space Sustainability

• URL: http://arxiv.org/abs/2508.00755v1
• Date: Fri, 01 Aug 2025 16:31:55 GMT
• Title: AI-Driven Collaborative Satellite Object Detection for Space Sustainability
• Authors: Peng Hu, Wenxuan Zhang,
• Abstract summary: The growing density of satellites in low-Earth orbit (LEO) presents serious challenges to space sustainability.<n>Traditional ground-based tracking systems are constrained by latency and coverage limitations.<n>We propose a novel satellite clustering framework that enables the collaborative execution of deep learning (DL)-based space object detection tasks across multiple satellites.
• Score: 29.817805350971366
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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