FPGA-Accelerated Neuromorphic Vision System for Real-Time Orbital Object Detection

• URL: http://arxiv.org/abs/2606.20727v1
• Date: Wed, 17 Jun 2026 04:34:11 GMT
• Title: FPGA-Accelerated Neuromorphic Vision System for Real-Time Orbital Object Detection
• Abstract summary: This work presents a comprehensive framework for neuromorphic resident space object (RSO) detection, adapting the foundational grid clustering algorithm for FPGA acceleration.<n>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.<n>We validate this architecture through systematic sampling of night-sky observations from the EVAS dataset, demonstrating 97% detection accuracy for RSOs.
• Score: 1.25052154879199
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 a foundational toolkit for event-based FPGA processing, achieves efficient throughput with a total power consumption of 8.5 W and deterministic processing latencies below 62 ms. The architecture's energy efficiency and high-precision detection position it as a viable solution for distributed space surveillance networks.

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