Event-based sensors, distinguished by their high temporal resolution of 1$\mathrm{\mu s}$ and a dynamic range of 120$\mathrm{dB}$, 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 a significant increase of 9.7\% over the previous best SNN. Moreover, the efficient design of SpikeFPN ensures robust performance while optimizing computational resources, attributed to its innate sparse computation capabilities.

翻译：基于事件驱动的传感器，凭借其1$\mathrm{\mu s}$的高时间分辨率和120$\mathrm{dB}$的动态范围，成为适用于车辆和无人机等高速场景的理想工具。传统利用人工神经网络（ANNs）的目标检测技术因这些传感器捕获事件的稀疏性和异步性而面临挑战。相比之下，脉冲神经网络（SNNs）提供了一种与事件数据天然对齐的时间表征，成为一种有前景的替代方案。本文探索了SNNs独特的膜电位动力学特性及其调节稀疏事件的能力，并提出了一种创新的脉冲触发自适应阈值机制以实现稳定训练。基于这些发现，我们构建了一个针对汽车事件目标检测优化的专用脉冲特征金字塔网络（SpikeFPN）。全面评估表明，SpikeFPN在性能上超越了传统SNNs及配备注意力机制的先进ANNs。值得注意的是，SpikeFPN在{GEN1汽车检测（GAD）}基准数据集上达到了0.477的均值平均精度（mAP），较此前最优SNN提升了9.7%。此外，SpikeFPN的高效设计凭借其内在的稀疏计算能力，在优化计算资源的同时确保了鲁棒性能。