Event-based sensors, distinguished by their high temporal resolution of 1 $\mathrm{\mu}\text{s}$ and a dynamic range of 120 $\text{dB}$, stand out as ideal tools for deployment in fast-paced settings like vehicles and drones. Traditional object detection techniques that utilize Artificial Neural Networks (ANNs) face challenges due to the sparse and asynchronous nature of the events these sensors capture. In contrast, Spiking Neural Networks (SNNs) offer a promising alternative, providing a temporal representation that is inherently aligned with event-based data. This paper explores the unique membrane potential dynamics of SNNs and their ability to modulate sparse events. We introduce an innovative spike-triggered adaptive threshold mechanism designed for stable training. Building on these insights, we present a specialized spiking feature pyramid network (SpikeFPN) optimized for automotive event-based object detection. Comprehensive evaluations demonstrate that SpikeFPN surpasses both traditional SNNs and advanced ANNs enhanced with attention mechanisms. Evidently, SpikeFPN achieves a mean Average Precision (mAP) of 0.477 on the GEN1 Automotive Detection (GAD) benchmark dataset, marking significant increases over the selected SNN baselines. Moreover, the efficient design of SpikeFPN ensures robust performance while optimizing computational resources, attributed to its innate sparse computation capabilities.

翻译：事件相机凭借其1微秒级的时间分辨率和120分贝的动态范围，在车辆和无人机等高速场景中展现出卓越的部署优势。传统基于人工神经网络的目标检测方法面对事件相机捕获的稀疏异步数据时存在技术挑战。相比之下，脉冲神经网络通过其与事件数据天然契合的时序表征能力提供了更具前景的解决方案。本文深入探究脉冲神经网络独特的膜电位动态特性及其对稀疏事件的调制能力，提出一种专为稳定训练设计的脉冲触发自适应阈值机制。基于这些发现，我们构建了面向自动驾驶事件检测的专用脉冲特征金字塔网络。全面评估表明，SpikeFPN在性能上超越了传统脉冲神经网络和配备注意力机制的先进人工神经网络。在GEN1自动驾驶检测基准数据集上，SpikeFPN取得了0.477的平均精度均值，较所选脉冲神经网络基线取得显著提升。值得注意的是，SpikeFPN凭借其固有的稀疏计算特性，在保持优异性能的同时优化了计算资源消耗。