Geometry-Consistent Content-Aware Fusion for Lightweight Small-Object Detection in UAV Imagery

Abstract

Small-object detection in unmanned aerial vehicle (UAV) imagery is challenging because targets are often tiny, densely distributed, and embedded in cluttered backgrounds, while edge platforms impose strict computational constraints. This paper presents a lightweight end-to-end detector based on RT-DETR to improve detection accuracy, localisation quality, and deployment efficiency. The proposed method includes three components: a detail-enhanced backbone for preserving fine-grained texture and edge cues, rotary positional encoding combined with a content-aware bidirectional feature pyramid for improved cross-scale alignment, and a difficulty-aware composite regression loss for more stable bounding-box optimisation. Experiments on VisDrone2019 and DOTA show that the proposed method improves mAP50 by 3.5% and 1.6%, respectively, over the RT-DETR baseline, while reducing the number of parameters by 28.6%. Ablation studies confirm the complementary contributions of the three components, and robustness experiments demonstrate reliable performance under challenging aerial conditions. Deployment on a Jetson Orin NX using a self-built UAV dataset further validates the practicality of the method for edge-side UAV monitoring.