FMRS-YOLO: A Feature-Modulated and Redundancy-Suppressed YOLO for UAV Remote Sensing Object Detection

Abstract

Unmanned aerial vehicle (UAV) remote sensing object detection remains challenging because aerial targets are often small, densely distributed, and embedded in complex background clutter, whereas onboard deployment requires compact computation and real-time inference. Existing real-time detectors commonly retain a conventional P3–P5 detection hierarchy, in which deep low-resolution stages may consume computation after fine-grained small-object cues have already been weakened. To address this issue, this paper presents feature-modulated and redundancy-suppressed YOLO (FMRS-YOLO), a UAV-oriented lightweight detector that improves the accuracy–efficiency trade-off through the redundancy-reduction (RR) scale allocation strategy and targeted feature refinement. Instead of extending the terminal hierarchy to the conventional P5 scale, FMRS-YOLO replaces the original stride-2 P5 transition with a stride-1 high-level transformation and omits the corresponding P5 prediction branch, thereby concentrating detection on the P3 and P4 feature levels where small aerial targets retain more informative spatial cues. To enhance feature representation under the compact RR scale allocation framework, we propose Gaussian cross-feature modulation (GCFM) to strengthen spatial–semantic interaction, and design attention-weighted parallel pyramid fusion (AWPPF) to improve clutter-aware multi-scale aggregation while preserving unpooled spatial details. Experiments are conducted separately on VisDrone-2019, a drone-based object detection benchmark used as the primary benchmark, and UCAS-AOD, an aerial object detection benchmark for aircraft and cars used as an independent complementary benchmark. Compared with scale-matched YOLOv11 baselines on VisDrone-2019, FMRS-YOLO improves mAP by 1.9–2.4 percentage points and mAP50 by 2.3–2.9 percentage points, while reducing parameters by 41.0–64.9%, reducing FLOPs by 1.6–20.7%, and increasing FPS by 9.3–10.4%. On UCAS-AOD, FMRS-YOLOn achieves 71.3% mAP, 98.8% mAP50, and 88.3% mAP75, showing favorable localization performance under a compact model scale. Ablation and visualization results further indicate that the proposed architecture improves foreground focus, suppresses background interference, and strengthens small-object localization. These results demonstrate that FMRS-YOLO provides a practical accuracy–efficiency trade-off for UAV remote sensing object detection.