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

• URL: http://arxiv.org/abs/2506.16436v1
• Date: Thu, 19 Jun 2025 16:15:05 GMT
• Title: An efficient neuromorphic approach for collision avoidance combining Stack-CNN with event cameras
• Authors: Antonio Giulio Coretti, Mattia Varile, Mario Edoardo Bertaina,
• Abstract summary: This work presents an innovative collision avoidance system utilizing event-based cameras.<n>The system, employing a Stack-CNN algorithm (previously used for meteor detection), analyzes real-time event-based camera data to detect faint moving objects.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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