To address the challenges of low accuracy, missed detections, and false positives in unmanned aerial vehicle (UAV) object detection, particularly under complex scenes and dense small-object distributions, this paper proposes a lightweight small-object detection framework named WFD-YOLOv11, built upon YOLOv11. First, a Feature Fusion Attention Network (FFA-Net) is introduced to preprocess UAV imagery, effectively mitigating image distortion and preserving discriminative features. Second, a novel C3k2-iAFF module with an iterative attention mechanism is designed to enhance multi-scale spatial and channel feature representation. Third, a dynamic head is employed to improve small-object localization accuracy while reducing model parameters. Finally, the WIoUv3 loss function is adopted to dynamically adjust bounding-box regression weights, further enhancing detection robustness. Experimental results on the VisDrone2019 dataset demonstrate that, compared with YOLOv11n, WFD-YOLOv11n improves mAP@0.5 and mAP@0.5:0.95 by 4.5% and 3.0%, respectively, while reducing parameter count by 11.6%. The proposed method exhibits superior performance in detecting densely distributed small objects under complex backgrounds, meeting the lightweight requirements of UAV-based applications.
分享
引用
