SpikeUAV-DETR: Energy-Efficient End-to-End Spike-Driven Object Detection for Unmanned Aerial Vehicle Imagery

Onboard detection for unmanned aerial vehicles (UAVs) can provide reliable, real-time detection results when network connectivity is limited. However, limited onboard resources require detectors to balance accuracy and energy consumption. Detectors based on spiking neural networks (SNNs) exploit spike-driven computation, resulting in lower energy consumption and making them promising for UAV applications. However, most real-time SNN detectors retain non-maximum suppression (NMS), which requires extensive tuning. Moreover, the discrete spike encoding inherent to SNNs leads to information loss and accuracy degradation. To address these challenges, we propose SpikeUAV-DETR, an end-to-end spike-driven detector that enables NMS-free inference. First, we employ time-channel adaptive firing thresholds that adjust to membrane-potential distributions, thereby improving spike information transmission. Second, we introduce high-resolution cross-level feature fusion to preserve fine-grained details and lightweight gating to inject high-level semantics, thereby enhancing small object detection. Third, we employ hybrid query selection and deformable attention modules to preserve precision with limited dense computation. Experimental results on two UAV imagery datasets show that SpikeUAV-DETR achieves a favorable accuracy-energy trade-off over representative detectors while retaining real-time inference, specifically improving mAP@50 by 1.1 percentage points over the ANN baseline RT-DETR-R18 on VisDrone2019-DET while reducing energy consumption by 52.4%.