# Sensing for Space Safety and Sustainability: A Deep Learning Approach with Vision Transformers

arXiv: https://arxiv.org/abs/2412.08913 · HTML: https://mlsanity.com/paper/2412.08913/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 13 references that are in the map.
- First posted on arXiv 12 Dec 2024.

## 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)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [YOLOX: Exceeding YOLO Series in 2021](https://mlsanity.com/paper/2107.08430/) (2021)
- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer](https://mlsanity.com/paper/2110.02178/) (2021)
- [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)
- [Knowledge Distillation in YOLOX-ViT for Side-Scan Sonar Object Detection](https://mlsanity.com/paper/2403.09313/) (2024)

## Built on by (most cited first, 4 of 4)

- [Toward Onboard AI-Enabled Solutions to Space Object Detection for Space Sustainability](https://mlsanity.com/paper/2505.01650/) (2025)
- [AI-Driven Collaborative Satellite Object Detection for Space Sustainability](https://mlsanity.com/paper/2508.00755/) (2025)
- [An Edge AI Solution for Space Object Detection](https://mlsanity.com/paper/2505.13468/) (2025)
- [Collaborative Space Object Detection with Multi-Satellite Viewpoints in LEO Constellations](https://mlsanity.com/paper/2606.01895/) (2026)

## Abstract (arXiv)

The rapid increase of space assets represented by small satellites in low Earth orbit can enable ubiquitous digital services for everyone. However, due to the dynamic space environment, numerous space objects, complex atmospheric conditions, and unexpected events can easily introduce adverse conditions affecting space safety, operations, and sustainability of the outer space environment. This challenge calls for responsive, effective satellite object detection (SOD) solutions that allow a small satellite to assess and respond to collision risks, with the consideration of constrained resources on a small satellite platform. This paper discusses the SOD tasks and onboard deep learning (DL) approach to the tasks. Two new DL models are proposed, called GELAN-ViT and GELAN-RepViT, which incorporate vision transformer (ViT) into the Generalized Efficient Layer Aggregation Network (GELAN) architecture and address limitations by separating the convolutional neural network and ViT paths. These models outperform the state-of-the-art YOLOv9-t in terms of mean average precision (mAP) and computational costs. On the SOD dataset, our proposed models can achieve around 95% mAP50 with giga-floating point operations (GFLOPs) reduced by over 5.0. On the VOC 2012 dataset, they can achieve ≥ 60.7% mAP50 with GFLOPs reduced by over 5.2.