  Spiking Neural Networks (SNNs) have garnered widespread interest for their
energy efficiency and brain-inspired event-driven properties. While recent
methods like Spiking-YOLO have expanded the SNNs to more challenging object
detection tasks, they often suffer from high latency and low detection
accuracy, making them difficult to deploy on latency sensitive mobile
platforms. Furthermore, the conversion method from Artificial Neural Networks
(ANNs) to SNNs is hard to maintain the complete structure of the ANNs,
resulting in poor feature representation and high conversion errors. To address
these challenges, we propose two methods: timesteps compression and
spike-time-dependent integrated (STDI) coding. The former reduces the timesteps
required in ANN-SNN conversion by compressing information, while the latter
sets a time-varying threshold to expand the information holding capacity. We
also present a SNN-based ultra-low latency and high accurate object detection
model (SUHD) that achieves state-of-the-art performance on nontrivial datasets
like PASCAL VOC and MS COCO, with about remarkable 750x fewer timesteps and 30%
mean average precision (mAP) improvement, compared to the Spiking-YOLO on MS
COCO datasets. To the best of our knowledge, SUHD is the deepest spike-based
object detection model to date that achieves ultra low timesteps to complete
the lossless conversion.
