  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. Source codes are
publicly accessible at https://github.com/EMI-Group/spikefpn.
