DPFS-YOLO: Missed-Detection Alleviation and False-Detection Risk Suppression for Small Objects in Complex UAV Aerial Scenes

Objects in images captured by unmanned aerial vehicles (UAVs) are often small in scale, weak in texture, and frequently occluded. Meanwhile, complex backgrounds can produce local responses similar to those of real targets, increasing the risk of missed detections for small objects and false detections in background regions. To address these challenges, this paper proposes DPFS-YOLO, a small-object detection method designed for complex aerial scenes. Built upon YOLOv8n, the proposed method integrates detail enhancement, foreground selection, and semantic guidance into a collaborative optimization framework, aiming to improve small-object feature representation while suppressing spurious background responses. First, the Dual-Path Edge Fusion (DPEF) module combines explicit edge priors, Gaussian smoothing constraints, and a learnable detail modeling branch to enhance object boundaries and local texture representations. Second, the Foreground-Guided Detail Selection (FGDS) module filters the enhanced detail responses through foreground gating, preserving target-relevant information while weakening false activations caused by complex backgrounds. Finally, the Semantic-Aware Guided Fusion for Tiny Object Detection (SAG-tiny) branch adds a P2 high-resolution detection layer and fuses shallow spatial details with top–down high-level semantic information before semantic-guided refinement, enabling small-object details to be constrained by semantic context during detection. Experiments on the VisDrone2019-DET, HIT-UAV, and TinyPerson datasets demonstrate that the proposed method effectively alleviates missed detections of small objects and reduces false-detection risk under complex background conditions.