摘要： 针对无人机影像中目标尺寸微小、背景复杂干扰强等问题，传统目标检测算法在特征提取与多尺度融合过程中容易出现特征退化与信息损失，从而导致检测精度下降。为此，本文提出了一种基于混合动态重参数化的微小目标检测算法（HDR-YOLO）。首先，由于传统卷积在微小目标特征提取上的局限，通过引入风车形卷积（Pinwheel-shaped Convolution, PConv）重构了C3K2-PC模块，显著增强了骨干网络对目标底层细节的感知与捕捉能力。其次，针对多尺度融合中的信息退化难题，本文设计了混合动态重参数化模块（Hybrid Dynamic Reparameterization Module, HDRep），通过低失真尺度变换与深层特征精炼相结合，实现高保真多尺度特征重建。基于此，进一步提出了全新的多尺度特征融合颈部结构（Multi-Scale Feature Fusion Neck, MSFPN），该结构通过优化跨层信息流，有效提升了模型在复杂背景下的鲁棒性。在 VisDrone2019 数据集上的实验结果表明，HDR-YOLO 在 mAP@50 和 mAP@50:95上分别达到 43.7% 和 26.5%，较 YOLOv11n 基线模型分别提升 10.2% 和 7.0%。同时，在公开的 AI-TOD 数据集及自建的 HVL-Cond 数据集上的实验进一步验证了所提算法的优越泛化性能与稳定性。

Abstract: Object detection in Unmanned Aerial Vehicle (UAV) imagery is severely challenged by tiny object sizes and strong background clutter, where traditional algorithms often suffer from feature degradation and information loss, leading to a decline in accuracy. To address these challenges, this paper proposes a tiny object detection algorithm based on hybrid dynamic reparameterization, termed HDR-YOLO. First, to overcome the limitations of conventional convolutions in extracting features from tiny objects, the C3K2-PC module is reconstructed by incorporating the Pinwheel-shaped Convolution (PConv), which significantly enhances the backbone network's ability to perceive and capture fine-grained details. Second, to tackle the problem of information degradation during multi-scale fusion, this work designs the Hybrid Dynamic Reparameterization Module (HDRep), which achieves high-fidelity multi-scale feature reconstruction through a combination of low-distortion scale transformation and deep feature refinement. Building upon this, a Multi-Scale Feature Fusion Neck (MSFPN) is introduced, which optimizes cross-scale information flow to effectively boost the model's robustness in complex backgrounds. Experimental results on the VisDrone-2019 dataset demonstrate that HDR-YOLO achieves an mAP@50 of 43.7% and an mAP@50:95 of 26.5%, outperforming the YOLOv11n baseline by 10.2% and 7.0%, respectively. Furthermore, experiments on the public AI-TOD dataset and a self-built HVL-Cond dataset validate the superior generalization and stability of the proposed algorithm.