Vision-Based Detection of Uncooperative Targets and Components on Small Satellites

TL;DR

This paper presents an autonomous vision-based detection system for uncooperative small satellite targets, combining YOLOv8 and knowledge distillation with Fast-SCNN to improve tracking accuracy across varying distances and conditions.

Contribution

It introduces a novel adaptive detection framework that integrates state-of-the-art CNN models and knowledge distillation for robust space object monitoring.

Findings

Effective detection across different distances and lighting conditions.

Real-time detection with low computational requirements.

Successful testing on space-simulated and public datasets.

Abstract

Space debris and inactive satellites pose a threat to the safety and integrity of operational spacecraft and motivate the need for space situational awareness techniques. These uncooperative targets create a challenging tracking and detection problem due to a lack of prior knowledge of their features, trajectories, or even existence. Recent advancements in computer vision models can be used to improve upon existing methods for tracking such uncooperative targets to make them more robust and reliable to the wide-ranging nature of the target. This paper introduces an autonomous detection model designed to identify and monitor these objects using learning and computer vision. The autonomous detection method aims to identify and accurately track the uncooperative targets in varied circumstances, including different camera spectral sensitivities, lighting, and backgrounds. Our method adapts to the relative distance between the observing spacecraft and the target, and different detection strategies are adjusted based on distance. At larger distances, we utilize You Only Look Once (YOLOv8), a multitask Convolutional Neural Network (CNN), for zero-shot and domain-specific single-shot real time detection of the target. At shorter distances, we use knowledge distillation to combine visual foundation models with a lightweight fast segmentation CNN (Fast-SCNN) to segment the spacecraft components with low storage requirements and fast inference times, and to enable weight updates from earth and possible onboard training. Lastly, we test our method on a custom dataset simulating the unique conditions encountered in space, as well as a publicly-available dataset.