A Robust Vision-based Algorithm for Detecting and Classifying Small Orbital Debris Using On-board Optical Cameras

摘要

This study develops a vision-based detection and classification algorithm to address the challenges of in-situ small orbital debris environment classification, including debris observability and instrument requirements for small debris observation. The algorithm operates in near real-time and is robust under challenging tasks in moving objects classification such as multiple moving objects, objects with various movement trajectories and speeds, very small or faint objects, and substantial background motion. The performance of the algorithm is optimized and validated using space image data available through simulated environments generated using NASA Marshall Space Flight Centers Dynamic Star Field Simulator of on-board optical sensors and cameras.