# AI-Driven Collaborative Satellite Object Detection for Space Sustainability

arXiv: https://arxiv.org/abs/2508.00755 · HTML: https://mlsanity.com/paper/2508.00755/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 12 references that are in the map.
- First posted on arXiv 1 Aug 2025.

## Builds on (most cited first, 8 of 8)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information](https://mlsanity.com/paper/2402.13616/) (2024)
- [Performance Study of YOLOv5 and Faster R-CNN for Autonomous Navigation around Non-Cooperative Targets](https://mlsanity.com/paper/2301.09056/) (2023)
- [Sensing for Space Safety and Sustainability: A Deep Learning Approach with Vision Transformers](https://mlsanity.com/paper/2412.08913/) (2024)
- [SatAIOps: Revamping the Full Life-Cycle Satellite Network Operations](https://mlsanity.com/paper/2305.08722/) (2023)
- [Toward Onboard AI-Enabled Solutions to Space Object Detection for Space Sustainability](https://mlsanity.com/paper/2505.01650/) (2025)

## Built on by (most cited first, 1 of 1)

- [Collaborative Space Object Detection with Multi-Satellite Viewpoints in LEO Constellations](https://mlsanity.com/paper/2606.01895/) (2026)

## Abstract (arXiv)

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.