YOLOv11-HNU: Enhanced Unmanned Aerial Vehicle Target Detection Based on Improved YOLOv11

Unmanned aerial vehicle (UAV) target detection has become increasingly critical across diverse applications including surveillance, search and rescue operations, and autonomous navigation. While the YOLO series models have demonstrated exceptional efficiency and real-time performance in object detection tasks, existing YOLOv11 models still exhibit limitations in complex UAV scenarios. To address these challenges, this paper proposes YOLOv11-HNU, an enhanced detection model incorporating two key innovations. First, we integrate the HFFBlock (Hierarchical Feature Fusion Block) module into the neck of YOLOv11 to enhance multi-scale feature fusion capabilities. Second, we implement the UIOU (Unified Intersection over Union) loss function to optimize training dynamics. Experimental results on the Roboflow UAVs dataset show that the HFFBlock module alone improves mAP@0.5 from 0.975 to 0.978, while the complete YOLOv11-HNU model maintains this optimal performance. On the more challenging Roboflow Drone-Kaggle-Data dataset, the HFFBlock module increases mAP@0.5 from 0.944 to 0.950, while the UIOU loss function alone achieves the best single improvement to 0.957. The final YOLOv11-HNU model, combining both innovations, achieves 0.951 mAP@0.5, representing a solid 0.7 percentage point improvement over the baseline. Comprehensive ablation studies reveal that the improvement strategies exhibit dataset-dependent characteristics, with HFFBlock providing consistent gains across datasets and UIOU showing particular effectiveness in complex detection scenarios. The proposed YOLOv11-HNU effectively enhances UAV target detection accuracy, providing a valuable technical solution for real-world UAV detection applications.