A Parameter-Compact Network with Hierarchical Detail Enhancement and High-Resolution Multiscale Fusion for Small Object Detection in UAV Imagery

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% mAP50–95, 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% mAP50–95, 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.