Attention-Guided Single-Stage Detector for Real-Time Object Detection on Edge-Computing UAV Systems

To address the challenge of balancing multiscale object recognition accuracy and computational efficiency for real-time detection tasks on unmanned aerial vehicle (UAV) edge-computing platforms, we propose an edge-attention synergy network (EASNet). First, a spatial pyramid information architecture is constructed to enable collaboration perception of local texture features and global semantic information through a cross-layer feature interaction mechanism. Second, a channel-adaptive dynamic weighting module is designed, leveraging a differential gating strategy to resolve semantic conflicts in multiresolution feature fusion. Finally, we propose a hybrid metric loss with learnable normalization. It dynamically adjusts Wasserstein scale sensitivity and incorporates fixed-weight IoU constraints, boosting small-target localization in complex backgrounds. The experimental results demonstrate that our method achieves mAP50 scores of 95.6%, 93.4%, 78.9%, 58.5%, and 46.5% on the RSOD, NWPU VHR-10, DIOR, VEDAI, and VisDrone datasets, respectively, with improvements ranging from 4.2% to 8.3% over the YOLOv10s baseline. Furthermore, through embedded deployment validation, we successfully implemented the model on a Jetson Nano edge-computing platform, attaining 34 FPS for real-time vehicle and pedestrian detection in campus monitoring scenarios.