An efficient neuromorphic approach for collision avoidance combining Stack-CNN with event cameras

TL;DR

This paper introduces a novel collision avoidance system using event-based cameras and a Stack-CNN algorithm to detect faint moving objects in space, enhancing real-time space situational awareness and traffic management.

Contribution

It combines event-based camera technology with a Stack-CNN approach for the first time in space debris detection, improving detection of faint objects in real-time.

Findings

Enhanced signal-to-noise ratio in terrestrial tests

Effective detection of faint moving objects

Potential for on-board space imaging improvements

Abstract

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.