  Spiking Neural Networks, as a third-generation neural network, are
well-suited for edge AI applications due to their binary spike nature. However,
when it comes to complex tasks like object detection, SNNs often require a
substantial number of time steps to achieve high performance. This limitation
significantly hampers the widespread adoption of SNNs in latency-sensitive edge
devices. In this paper, our focus is on generating highly accurate and
low-latency SNNs specifically for object detection. Firstly, we systematically
derive the conversion between SNNs and ANNs and analyze how to improve the
consistency between them: improving the spike firing rate and reducing the
quantization error. Then we propose a structural replacement, quantization of
ANN activation and residual fix to allevicate the disparity. We evaluate our
method on challenging dataset MS COCO, PASCAL VOC and our spike dataset. The
experimental results show that the proposed method achieves higher accuracy and
lower latency compared to previous work Spiking-YOLO. The advantages of SNNs
processing of spike signals are also demonstrated.
