# Toward Onboard AI-Enabled Solutions to Space Object Detection for Space Sustainability

arXiv: https://arxiv.org/abs/2505.01650 · HTML: https://mlsanity.com/paper/2505.01650/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 17 references that are in the map.
- First posted on arXiv 3 May 2025.

## Builds on (most cited first, 10 of 10)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [Opening the Black Box of Deep Neural Networks via Information](https://mlsanity.com/paper/1703.00810/) (2017)
- [YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information](https://mlsanity.com/paper/2402.13616/) (2024)
- [Scaled ReLU Matters for Training Vision Transformers](https://mlsanity.com/paper/2109.03810/) (2021)
- [Transformers in Small Object Detection: A Benchmark and Survey of State-of-the-Art](https://mlsanity.com/paper/2309.04902/) (2023)
- [Towards Flexible Inductive Bias via Progressive Reparameterization Scheduling](https://mlsanity.com/paper/2210.01370/) (2022)
- [Sensing for Space Safety and Sustainability: A Deep Learning Approach with Vision Transformers](https://mlsanity.com/paper/2412.08913/) (2024)

## Built on by (most cited first, 2 of 2)

- [AI-Driven Collaborative Satellite Object Detection for Space Sustainability](https://mlsanity.com/paper/2508.00755/) (2025)
- [Collaborative Space Object Detection with Multi-Satellite Viewpoints in LEO Constellations](https://mlsanity.com/paper/2606.01895/) (2026)

## Abstract (arXiv)

The rapid expansion of advanced low-Earth orbit (LEO) satellites in large constellations is positioning space assets as key to the future, enabling global internet access and relay systems for deep space missions. A solution to the challenge is effective space object detection (SOD) for collision assessment and avoidance. In SOD, an LEO satellite must detect other satellites and objects with high precision and minimal delay. This paper investigates the feasibility and effectiveness of employing vision sensors for SOD tasks based on deep learning (DL) models. It introduces models based on the Squeeze-and-Excitation (SE) layer, Vision Transformer (ViT), and the Generalized Efficient Layer Aggregation Network (GELAN) and evaluates their performance under SOD scenarios. Experimental results show that the proposed models achieve mean average precision at intersection over union threshold 0.5 (mAP50) scores of up to 0.751 and mean average precision averaged over intersection over union thresholds from 0.5 to 0.95 (mAP50:95) scores of up to 0.280. Compared to the baseline GELAN-t model, the proposed GELAN-ViT-SE model increases the average mAP50 from 0.721 to 0.751, improves the mAP50:95 from 0.266 to 0.274, reduces giga floating point operations (GFLOPs) from 7.3 to 5.6, and lowers peak power consumption from 2080.7 mW to 2028.7 mW by 2.5%.