Object detection in unmanned aerial vehicle (UAV) imagery is severely challenged by extremely small object scales, cluttered backgrounds, and pronounced foreground–background imbalance, which jointly degrade the accuracy of general-purpose detectors. This paper presents HSF-Net, a small-object detection network built upon YOLOv11s through three complementary enhancements. First, a Hierarchical Edge Enhancement Module (HEEM) employs orthogonal strip-convolution decomposition with zero-initialized residual fusion to enhance and re-weight the fine-scale edge and texture cues that are progressively attenuated in deep convolutional backbones. Second, a Sparse-Aware Feature Modulation (SAFM) module replaces concatenation-based fusion in the top-down neck pathway, coupling a sparse foreground gate with channel-wise scale modulation to confine cross-scale aggregation to object-bearing regions. Third, the detection head is restructured from {P3, P4, P5} to {P2, P3, P4}, introducing a high-resolution pathway for tiny targets while removing the original low-resolution P5 branch. On VisDrone, HSF-Net attains 46.4% mAP₅₀ and 28.5% mAP_50–95, exceeding the baseline by 7.1 and 4.8 percentage points, respectively, while reducing the parameter count from 9.4 million to 3.6 million. The model achieves an end-to-end throughput of 131.3 FPS on an NVIDIA RTX 3090, although the high-resolution P2 branch increases the computational cost to 39.5 GFLOPs. After dataset-specific training and evaluation on the markedly different TinyPerson benchmark, HSF-Net outperforms YOLOv11s by 5.2 percentage points in mAP₅₀, indicating that its relative performance advantage persists under a substantially different target-scale distribution and maritime background.

Small-object detection in UAV imagery remains challenging because repeated downsampling weakens fine structural cues, conventional feature pyramids are poorly aligned with small-object-dominated scale distributions, and fixed detection heads provide limited adaptation. To address these issues, a Hierarchical–Bidirectional–Dynamic Network (HBD-Net) is developed as a parameter-compact detector based on YOLOv11s. HEEM reinforces edge, contour, and texture information at multiple backbone stages using orthogonal depthwise strip convolutions, residual detail extraction, and zero-initialized fusion. PLSS replaces the conventional P3–P5 hierarchy with P2–P4 to emphasize high-resolution features, while P2-BiFPN introduces bidirectional propagation and learnable weighted fusion. An integrated Dynamic Head further refines features across scale, spatial, and task dimensions. On VisDrone, HBD-Net achieves 39.0% mAP₅₀ and 22.6% mAP_50–95, improving the YOLOv11s baseline by 6.9 and 4.3 percentage points, respectively, while reducing parameters from 9.4 M to 3.7 M. Ablation studies confirm the complementary effects of the components. On HIT-UAV, HBD-Net reaches 84.1% mAP₅₀ and 57.0% mAP_50–95, indicating effectiveness under infrared aerial imaging. These results demonstrate that coordinated structural enhancement, high-resolution multiscale fusion, and adaptive prediction can improve UAV small-object detection with a compact parameter footprint.