Event-Fused Hybrid ANN-SNN Architecture for Low-Latency Object Detection in Automotive Vision

In advanced driver-assistance systems, current computer vision algorithms predominantly rely on frame-based RGB cameras, which suffer from high latency in high-speed or sudden-scenario applications due to fixed frame rates. In response to this challenge, event-based cameras have gained attention as a viable substitute, providing markedly higher temporal resolution and greatly diminished latency. However, the asynchronous and sparse nature of event data poses challenges in achieving accuracy comparable to frame-based algorithms. Leveraging the event-driven nature of Spiking Neural Networks (SNNs), we propose an Event-Fused Hybrid (EFH) architecture for automotive vision. EFH combines Artificial Neural Networks (ANNs) for static feature extraction from RGB frames with SNNs that dynamically update these features using event streams. This approach enables high-efficiency, high-frame-rate object detection with minimal latency. Our method achieves state-of-the-art performance in inter-frame object detection by effectively fusing event data, while the SNN branch significantly reduces power consumption during event-stream processing. Furthermore, we deploy the system on a vehicle platform, achieving real-time object detection at 60 FPS using a 15-FPS RGB camera paired with an event camera. The code is publicly available athttps://github.com/zhangcj13/EFHhttps://github.com/zhangcj13/EFH.