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.

The unmanned aerial vehicle (UAV) localization method based on global features is a fast and efficient approach for satellite-denied environments. Such methods typically extract global features from aerial images and retrieve matches from a constructed feature database to locate UAVs. However, constructing the feature database requires traversing the entire map, leading to storage redundancy. Moreover, the reference images in the database often have fixed fields of view and orientations, making it difficult to adapt to the changes in aerial images caused by the altitude and attitude changes of the UAV. To address these challenges, this paper explores the uniqueness of semantic instances within the mission region and proposes a UAV localization method based on unique semantic instances. The proposed method first extracts the labels of unique semantic instances from aerial images. These labels are then used to retrieve and match the corresponding feature vectors stored in the database. The location is determined based on the centroid positions of the matched unique semantic instances stored in the feature vectors. Experimental results on both simulation and flight datasets show that the proposed method achieves a localization success rate exceeding 95% in the mission region and remains robust to changes in the attitude and field of view of aerial images. The proposed method requires storing only the categories and locations of the instances, significantly reducing data storage requirements.