AI-Driven Collaborative Satellite Object Detection for Space Sustainability

TL;DR

This paper introduces a collaborative, AI-powered satellite detection framework that improves space object identification in LEO by using multiple satellites working together, supported by a new dataset and optimized detection strategies.

Contribution

The paper presents a novel satellite clustering framework for collaborative deep learning-based space object detection, along with a new high-fidelity dataset and a distance-aware viewpoint selection strategy.

Findings

Achieves detection accuracy comparable to single-satellite methods

Maintains low size, weight, and power footprint

Enhances space situational awareness through distributed AI systems

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.