SpikeGate-YOLO: Spiking Object Detection With Dynamic Gating and Multigranularity Fusion

Spiking Neural Networks (SNNs) transmit information via discrete spike events, offering advantages in energy efficiency and computational cost. However, current SNN-based object detectors suffer from limited feature expression and inefficient fusion due to temporal sparsity and the asynchronous nature of spike features. To address these challenges, we propose SpikeGate-YOLO, a spiking object detection architecture optimized for spike-driven processing. Specifically, we introduce the Reparam-Spike Gating (RSG) block to enhance feature expressiveness while maintaining computational efficiency. We also design the Spike Multi-Granularity Difference-aware Feature Harmonizer (SpikeMDFH), which improves multi-scale feature fusion through dynamic attention and biologically inspired gating mechanisms, preserving spike sparsity. Experiments on both the COCO and Gen1 datasets show that SpikeGate-YOLO achieves state-of-the-art results, reaching 63.4% mAP@50 and 46.3% mAP@50:95 on COCO, and 69.3% mAP@50 and 42.9% mAP@50:95 on Gen1. These results confirm the effectiveness of our architecture in overcoming spike-specific limitations in feature representation and fusion for object detection.