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

TL;DR

This paper explores the use of deep learning vision models for space object detection on LEO satellites, demonstrating improved accuracy and efficiency for collision avoidance to support space sustainability.

Contribution

It introduces novel deep learning models based on SE, ViT, and GELAN architectures for space object detection, with experimental validation showing enhanced performance and reduced power consumption.

Findings

GELAN-ViT-SE model achieves up to 0.751 mAP50 score.

Proposed models reduce GFLOPs and power consumption.

Experimental results outperform baseline models in accuracy and efficiency.

Abstract

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\%.