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.
To tackle the challenge of detecting small targets in UAV imagery, this paper proposes PF-DETR, an enhanced object detection model based on RT-DETR, designed to improve detection accuracy in complex scenes. The improvements are primarily reflected in the following aspects. First, a P2 detection head is added to extend the feature pyramid to finer scales, thereby enhancing the ability of shallow features to detect small targets. Second, a Pyramidal Hierarchical Frequency-Domain Fusion (PHF) module is introduced. By combining wavelet pooling with high- and low-frequency attention, the module effectively extracts and fuses multi-scale features, reduces information loss, and improves detection accuracy for small targets. Finally, the backbone network is restructured through the design of a lightweight BasicBlock_FasterNet_Rep module, which integrates FasterNet and RepVGG-style re-parameterization. This restructuring significantly reduces model complexity and parameters while strengthening multi-scale feature extraction. Experimental results on the VisDrone2019 dataset show that the improved PF-DETR achieves a notable performance boost: compared to the original model, mAP@0.5 increases by 5.4%, while the number of parameters is reduced by 25.0%. The computational cost increases by about 35.0%, but this comes with higher accuracy, resulting in a favorable balance between detection performance and model efficiency. Overall, these improvements enhance the model’s robustness and accuracy in detecting multi-scale and small targets in complex and cluttered scenes.
