An efficient neuromorphic approach for collision avoidance combining Stack-CNN with event cameras
摘要
Space debris poses a significant threat, driving research into active and passive mitigation strategies. This work presents an innovative collision avoidance system utilizing event-based cameras - a novel imaging technology well-suited for Space Situational Awareness (SSA) and Space Traffic Management (STM). The system, employing a Stack-CNN algorithm (previously used for meteor detection), analyzes real-time event-based camera data to detect faint moving objects. Testing on terrestrial data demonstrates the algorithm's ability to enhance signal-to-noise ratio, offering a promising approach for on-board space imaging and improving STM/SSA operations.
引用
@article{arxiv.2506.16436,
title = {An efficient neuromorphic approach for collision avoidance combining Stack-CNN with event cameras},
author = {Antonio Giulio Coretti and Mattia Varile and Mario Edoardo Bertaina},
journal= {arXiv preprint arXiv:2506.16436},
year = {2025}
}
备注
18th International Conference on Space Operations - Safety and sustainability of Space Operations (SSU)