AQF-Net: Adaptive query modeling and efficient feature fusion for UAV tiny-object detection

Abstract

Tiny-object detection in UAV aerial imagery remains challenging due to extremely small object scales, dense distributions, and complex backgrounds. Existing methods often suffer from inefficient query modeling and inadequate multi-scale feature representation, particularly in high-resolution scenarios with substantial variations in target density. To address these challenges, this paper proposes AQF-Net, a unified detection framework built upon the D-FINE architecture. AQF-Net integrates three key components: a Fixed-Query Self-Attention (FQSA) mechanism for efficient global context modeling, a Large-Receptive-Field Enhancement (LREA) module for enhanced multi-scale feature fusion, and an adaptive query modeling strategy for density-aware query allocation. These components are tightly coupled to jointly optimize feature representation and query generation, enabling the model to better adapt to complex UAV scenarios. Extensive experiments are conducted on the CODrone, VisDrone2019, and a self-constructed photovoltaic defect dataset (PV-DV). The results demonstrate that AQF-Net consistently outperforms the D-FINE baseline and several state-of-the-art methods in both overall detection accuracy and tiny-object detection capability. Notably, AQF-Net achieves 33.4% AP and 55.0% AP50 on the VisDrone2019 validation set, while maintaining a favorable balance between accuracy and computational efficiency.