Enhancing UAV small-object detection via spatial–frequency synergy and polarity-aware attention
摘要
Unmanned aerial vehicle (UAV) object detection is a critical task in visual computing, underpinning applications in traffic surveillance, disaster response, and precision agriculture. However, this task is uniquely challenging due to the aerial perspective, which often renders small objects as clusters of only tens of pixels, with blurred edges and weak signals easily overwhelmed by complex, cluttered backgrounds. Current detection frameworks frequently struggle to reconcile the need for fine-grained detail capture with the stringent real-time demands of UAV platforms. To address this, we introduce SF-DETR, an end-to-end Detection Transformer framework designed for robust and efficient small-object detection in UAV imagery. Methodologically, our contribution is twofold. First, we propose a lightweight backbone network, SFH-MFF, which embeds a learnable frequency filtering module within shallow layers to explicitly amplify subtle high-frequency textures, achieving a complementary feature representation across spatial and frequency domains. Second, we design a dual-path linear attention module, DL-AFIM, within the encoder. This module employs a positive–negative perception mechanism to effectively distinguish foreground targets from background clutter, while an integrated frequency-domain feed-forward network and an adaptive multi-scale residual fusion strategy enhance high-frequency detail representation without increasing parameter burden. Here we show that on the VisDrone and UAVDT datasets, our method achieves a 1.8% and 1.5% improvement in AP, and a 2.6% and 2.1% gain in