FPGA-Accelerated Neuromorphic Vision System for Real-Time Orbital Object Detection
The escalating congestion in orbital space demands advanced monitoring solutions. This work presents a comprehensive open-source framework for neuromorphic resident space object (RSO) detection, adapting the foundational grid clustering algorithm for FPGA acceleration. The system integrates a single event-based camera (EBC) with a custom, distributed processing architecture, where rapid spatial quantization is executed in programmable logic (FPGA) and cluster formation is managed by a software client. We validate this architecture through systematic sampling of night-sky observations from the EVAS dataset, demonstrating 97% detection accuracy for RSOs. The implementation, which serves as ...
| Authors | Diego Hernández, Sebastián Valdivia, Vicente Westerhout et al. |
|---|---|
| Primary category | cs.AR |
| Published | 17 June 2026 |
| Last updated at source | 17 June 2026 |
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