用事件相机+FPGA实现实时太空物体高精度检测
FPGA-Accelerated Neuromorphic Vision System for Real-Time Orbital Object Detection
- 事件相机配合FPGA实现快速空间量化与集群处理
- 在EVAS数据集上达97%检测准确率，延迟低于62毫秒
- 适合分布式太空监测网络，功耗仅8.5瓦
轨道空间日益拥挤，亟需先进监测手段。本文提出一个开源的类脑计算框架，用于实时检测轨内空间物体（RSO），将基础网格聚类算法适配至FPGA加速。系统结合单个事件相机（EBC）与定制分布式处理架构，其中空间量化在可编程逻辑（FPGA）中完成，聚类由软件客户端管理。通过在EVAS数据集上对夜间天空观测进行系统采样验证，该架构在识别RSO方面达到97%的准确率。实现总功耗8.5 W，处理延迟确定性低于62毫秒。该设计具备高能效与高精度，适用于分布式空间监视网络。
原文摘要 · Abstract (English)
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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