  Spiking neural networks (SNNs) are brain-inspired energy-efficient models
that encode information in spatiotemporal dynamics. Recently, deep SNNs trained
directly have shown great success in achieving high performance on
classification tasks with very few time steps. However, how to design a
directly-trained SNN for the regression task of object detection still remains
a challenging problem. To address this problem, we propose EMS-YOLO, a novel
directly-trained SNN framework for object detection, which is the first trial
to train a deep SNN with surrogate gradients for object detection rather than
ANN-SNN conversion strategies. Specifically, we design a full-spike residual
block, EMS-ResNet, which can effectively extend the depth of the
directly-trained SNN with low power consumption. Furthermore, we theoretically
analyze and prove the EMS-ResNet could avoid gradient vanishing or exploding.
The results demonstrate that our approach outperforms the state-of-the-art
ANN-SNN conversion methods (at least 500 time steps) in extremely fewer time
steps (only 4 time steps). It is shown that our model could achieve comparable
performance to the ANN with the same architecture while consuming 5.83 times
less energy on the frame-based COCO Dataset and the event-based Gen1 Dataset.
