An efficient neuromorphic approach for collision avoidance combining Stack-CNN with event cameras
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911014873726976 |
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| author | Coretti, Antonio Giulio Varile, Mattia Bertaina, Mario Edoardo |
| author_facet | Coretti, Antonio Giulio Varile, Mattia Bertaina, Mario Edoardo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_16436 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An efficient neuromorphic approach for collision avoidance combining Stack-CNN with event cameras Coretti, Antonio Giulio Varile, Mattia Bertaina, Mario Edoardo Machine Learning 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. |
| title | An efficient neuromorphic approach for collision avoidance combining Stack-CNN with event cameras |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2506.16436 |