Spiking Neural Networks (SNNs) have attracted
significant attention for their energy-efficient and brain-inspired
event-driven properties. Recent advancements, notably Spiking-
YOLO, have enabled SNNs to undertake advanced object
detection tasks. Nevertheless, these methods often suffer from
increased latency and diminished detection accuracy, rendering
them less suitable for latency-sensitive mobile platforms. Additionally,
the conversion of artificial neural networks (ANNs)
to SNNs frequently compromises the integrity of the ANNs’
structure, resulting in poor feature representation and heightened
conversion errors. To address the issues of high latency and
low detection accuracy, we introduce two solutions: timestep
compression and spike-time-dependent integrated (STDI) coding.
Timestep compression effectively reduces the number of timesteps
required in the ANN-to-SNN conversion by condensing information.
The STDI coding employs a time-varying threshold to
augment information capacity. Furthermore, we have developed
an SNN-based spatial pyramid pooling (SPP) structure, optimized
to preserve the network’s structural efficacy during conversion.
Utilizing these approaches, we present the ultralow latency and
highly accurate object detection model, SUHD. SUHD exhibits
exceptional performance on challenging datasets like PASCAL
VOC and MS COCO, achieving a remarkable reduction of
approximately 750 times in timesteps and a 30% enhancement in
mean average precision (mAP) compared to Spiking-YOLO on
MS COCO. To the best of our knowledge, SUHD is currently the
deepest spike-based object detection model, achieving ultralow
timesteps for lossless conversion.