SpikSSD: Better Extraction and Fusion for Object Detection with Spiking Neuron Networks

Yimeng Fan, Changsong Liu, Mingyang Li, Wei Zhang

Keywords: Spiking Neural Networks, Object Detection, Membrane-based Deformed Shortcut, Spiking Bi-direction Fusion Module

Abstract: As the third generation of neural networks, Spiking Neural Networks (SNNs) have gained widespread attention due to their low energy consumption and biological interpretability. Recently, SNNs have made considerable advancements in computer vision. However, efficiently conducting feature extraction and fusion under the spiking characteristics of SNNs for object detection remains a pressing challenge. To address this problem, we propose the SpikSSD, a novel Spiking Single Shot Multibox Detector. Specifically, we design a full-spiking backbone network, MDS-ResNet, which effectively adjusts the membrane synaptic input distribution at each layer, achieving better spiking feature extraction. Additionally, for spiking feature fusion, we introduce the Spiking Bi-direction Fusion Module (SBFM), which for the first time realizes bi-direction fusion of spiking features, enhancing the multi-scale detection capability of the model. Experimental results show that SpikSSD achieves 40.8\% mAP on the GEN1 dataset and 76.0\% mAP@0.5 on the VOC 2007 dataset with only around 10\% firing rate, outperforming existing SNN-based approaches at ultralow energy consumption. This work sets a new benchmark for future research in SNN-based object detection. Our code is publicly available in supplementary materials.

Primary Area: applications to neuroscience & cognitive science

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Submission Number: 4564