SpikeRED: Fully Spike-Driven RGB-Event Object Detection with Event-Guided Fusion and Box-Guided Query Refinement

Spiking neural networks (SNNs) offer substantial potential for energy-efficient visual detection. However, existing spiking object detectors primarily rely on unimodal inputs, while existing multimodal spiking approaches typically retain ANN components, limiting their energy-efficiency advantages. To address this issue, we propose SpikeRED, a fully spike-driven RGB-event object detection framework. Its Asymmetric Spiking Fusion (ASF) module enables more effective Event-to-RGB interaction with lower energy overhead. The Box-Guided Spiking Decoder further enhances the representation capability of spiking object queries through reference-box guidance. Experiments on DSEC-Det and PKU-DAVIS-SOD demonstrate that SpikeRED achieves performance comparable to leading multimodal ANN detectors while substantially reducing theoretical inference energy. To our knowledge, SpikeRED is the first fully spike-driven query-based multimodal object detection framework.