Object detection in UAV imagery presents a significant challenge due to low image resolution, complex visual backgrounds, and substantial inter-object scale variation. Detection Transformer (DETR)-based models often underperform in such scenarios. This paper proposes Pre-Fusion Guided DETR (PF-DETR), a refined and enhanced extension of the D-FINE model, specifically designed for robust small-object detection in UAV imagery. PF-DETR introduces a novel Pre-Encoded Feature Fusion (PEFF) module, which employs a unidirectional fusion strategy to integrate low-level spatial features into high-level semantic features, thereby improving the model’s ability to capture fine-grained details of small objects. Additionally, a Feature Enhancement Attention (FEA) mechanism is incorporated to strengthen feature representation and reduce background interference. To address information loss during multi-scale processing, a Haar Wavelet Downsampling (HWD) module is proposed, combining Haar wavelet transforms with convolutional downsampling to better preserve critical features. Furthermore, the original GIoU loss is replaced with an Enhanced IoU (EIoU) loss function to improve localization accuracy and training efficiency. Experimental results on the challenging VisDrone2019 benchmark demonstrate that PF-DETR outperforms state-of-the-art detectors, including DEIM, D-FINE, YOLOv10, and YOLOv11, in both detection accuracy and robustness. Specifically, PF-DETR achieves an AP50 of 40.6% and an AP of 23.6%, representing improvements of 2.0% and 1.5% over D-FINE, 2.7% and 1.8% over DEIM, and notable gains of 7.6% and 8.3% in AP50 compared to YOLOv11n and YOLOv10s, respectively. These results underscore the strong potential of PF-DETR for real-world UAV applications requiring precise small-object detection under challenging conditions.