Refining Small Object Detection in Aerial Images with PF-DETR: A Progressive Fusion Approach

Small object detection remains a challenging task due to limited pixel resolution, complex backgrounds, and high sensitivity to bounding box variations in aerial images. This study proposed PF DETR, a model specifically designed to refine small object detection through progressive feature fusion techniques. Central to our approach is the S2-CCFF (Cross-Scale Feature Fusion with S2) module, which integrates multi-level features with an S2 layer to preserve small object details. Coupled with SPDConv downsampling, this module reduces computational cost while maintaining critical information. Additionally, proposed CSPOK-Fusion mechanism captures a diverse range of global, local, and large-scale features, effectively mitigating background interference and occlusion effects to enhance cross-scale spatial repre sentation. We further introduce a Parallelized Patch-Aware (PPA) attention module in the Backbone network to prioritize small object features, significantly addressing information loss. Finally, Normalized Wasserstein Distance (NWD) loss function is incorporated to heighten robustness against minor localization errors by aligning bounding box positioning and shape, thus boosting detection accuracy. Experimental results on the VisDrone and NWPU VHR-10 datasets reveal that PF-DETR surpasses existing state-of-the-art methods, establishing its effectiveness and adaptability in complex aerial detection tasks.