Event-based sensors, distinguished by their high temporal resolution of 1 {\mu}s and a dynamic range of 120 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：基于事件的传感器凭借其1微秒的高时间分辨率和120dB的动态范围，成为部署在车辆和无人机等高速场景中的理想工具。传统利用人工神经网络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，脉冲神经网络提供了一种有前景的替代方案，其时间表示与事件数据天然对齐。本文探讨了脉冲神经网络独特的膜电位动态特性及其调制稀疏事件的能力，并引入了一种专为稳定训练设计的脉冲触发自适应阈值机制。基于这些见解，我们提出了一种专用于汽车事件目标检测的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脉冲神经网络和增强注意力机制的先进人工神经网络。具体而言，SpikeFPN在GEN1汽车检测（GAD）基准数据集上取得了0.477的平均精度（mAP），相比之前最优的脉冲神经网络提升了9.7%。此外，得益于其固有的稀疏计算能力，SpikeFPN的高效设计在优化计算资源的同时确保了稳健性能。