SpikeDet: Better Firing Patterns for Accurate and Energy-Efficient Object Detection with Spiking Neuron Networks

Spiking Neural Networks (SNNs) are the third generation of neural networks. They have gained widespread attention in object detection due to their low power consumption and biological interpretability. However, existing SNN-based object detection methods suffer from local firing saturation, where neurons in information-concentrated regions fire continuously throughout all time steps. This abnormal neuron firing pattern reduces the feature discrimination capability and detection accuracy, while also increasing the firing rates that prevent SNNs from achieving their potential energy efficiency. To address this problem, we propose SpikeDet, a novel spiking object detector that optimizes firing patterns for accurate and energy-efficient detection. Specifically, we design a spiking backbone network, MDSNet, which effectively adjusts the membrane synaptic input distribution at each layer, achieving better neuron firing patterns during spiking feature extraction. Additionally, to better utilize and preserve these high-quality backbone features, we introduce the Spiking Multi-direction Fusion Module (SMFM), which realizes multi-direction fusion of spiking features, enhancing the multi-scale detection capability of the model. Experimental results demonstrate that SpikeDet achieves superior performance. On the COCO 2017 dataset, it achieves 51.4% AP, outperforming previous SNN-based methods by 2.5% AP while requiring only half the power consumption. On object detection sub-tasks, including the GEN1 event-based dataset and the URPC 2019 underwater dataset, SpikeDet also achieves the best performance. Notably, on GEN1, our method achieves 47.6% AP, outperforming previous SNN-based methods by 7.2% AP with better energy efficiency.