A Dedicated Lightweight Network with Synergistic Attention for Precise Air-to-Air UAV Detection

The rapid advancement of unmanned aerial vehicle (UAV) technology has made air-to-air UAV object detection increasingly essential. However, the model faces additional challenges including small target sizes, motion blur, illumination variations, and stringent real-time performance requirements under constrained computational resources. To address these challenges, this paper proposes A2A-YOLO, a specialized detection model that introduces LECA-Conv for local and channel feature enhancement to effectively mitigate motion blur and illumination variations while incorporating GhostModulev2 for efficient feature extraction and Tiny Detection Heads for improved small target recognition. The proposed LECA-Conv module operates on the principle that attention parameters need not directly modify original feature maps, a key insight validated through extensive experiments. Extensive evaluations on the Det-Fly dataset demonstrate A2A-YOLO’s superior performance with 85.0% precision (PP), 80.7% recall (PR), and 81.9% average precision (AP), outperforming YOLO11 by 0.9%, 8.4%, and 6.5%, respectively. The proposed method demonstrates outstanding performance across diverse backgrounds and challenging conditions including motion blur and illumination variations. The model achieves real-time detection at 15 FPS on RK3588 platform while delivering remarkable performance in infrared small target detection.