  Event cameras, characterized by high temporal resolution, high dynamic range,
low power consumption, and high pixel bandwidth, offer unique capabilities for
object detection in specialized contexts. Despite these advantages, the
inherent sparsity and asynchrony of event data pose challenges to existing
object detection algorithms. Spiking Neural Networks (SNNs), inspired by the
way the human brain codes and processes information, offer a potential solution
to these difficulties. However, their performance in object detection using
event cameras is limited in current implementations. In this paper, we propose
the Spiking Fusion Object Detector (SFOD), a simple and efficient approach to
SNN-based object detection. Specifically, we design a Spiking Fusion Module,
achieving the first-time fusion of feature maps from different scales in SNNs
applied to event cameras. Additionally, through integrating our analysis and
experiments conducted during the pretraining of the backbone network on the
NCAR dataset, we delve deeply into the impact of spiking decoding strategies
and loss functions on model performance. Thereby, we establish state-of-the-art
classification results based on SNNs, achieving 93.7\% accuracy on the NCAR
dataset. Experimental results on the GEN1 detection dataset demonstrate that
the SFOD achieves a state-of-the-art mAP of 32.1\%, outperforming existing
SNN-based approaches. Our research not only underscores the potential of SNNs
in object detection with event cameras but also propels the advancement of
SNNs. Code is available at https://github.com/yimeng-fan/SFOD.
