Spiking the transformer: NeuViT with single-step attention for efficient high-resolution UAV vision

High-resolution aerial imagery from unmanned aerial vehicles (UAVs) provides the fine-grained spatial detail required for reliable perception during field missions, yet onboard processing of such data is constrained by high computational, latency, and energy demands. We present Neuromorphic Vision Transformer (NeuViT), a spiking vision transformer tailored for real-time high-resolution object detection under the strict power and latency constraints of UAV platforms. NeuViT integrates a single-step stateless spiking paradigm that removes temporal dependencies and membrane state tracking, and a spike-driven multi-head self-attention mechanism that replaces dense attention with binary spike coincidences, eliminating all multiply-accumulate operations. Evaluated on the VisDrone2019 dataset at 1500 × 2000 resolution, NeuViT achieves 88.8 % lower energy consumption and 31 % lower latency than Swin Transformer, while retaining 79.4 % of its detection accuracy. NeuViT remains fully operational within a 10 W power budget and sustains more than 22 frames per second, whereas baseline vision transformers, efficient convolutional neural networks, and spiking neural networks fail to meet UAV deployment requirements. Through per-class precision analysis, confusion matrix characterization, and spike distribution correlation, we identify that accuracy loss concentrates on small, low-contrast objects where spike thresholds suppress weak activations, informing an enhancement roadmap. Energy projections across 45 nm, 16 nm, and 7 nm technology nodes confirm that NeuViT maintains sub-10 W operation regardless of fabrication process. These results demonstrate that neuromorphic principles, when pragmatically adapted to silicon constraints, can enable high-resolution vision capabilities in resource-constrained aerial platforms. The code and models are publicly available at https://github.com/jayshent/NeuViT.