Abstract
Training object detectors demands extensive, task-specific annotations, yet this requirement becomes impractical in UAV-based human detection due to constantly shifting target distributions and the scarcity of labeled images. As a remedy, synthetic simulators are adopted to generate annotated data, with a low annotation cost. However, the domain gap between synthetic and real images hinders the model from being effectively applied to the target domain. Accordingly, we introduce Coarse-to-Fine Hierarchical Alignment (CFHA), a three-stage diffusion-based framework designed to transform synthetic data for UAV-based human detection, narrowing the domain gap while preserving the original synthetic labels. CFHA explicitly decouples global style and local content domain discrepancies and bridges those gaps using three modules: (1) Global Style Transfer -- a diffusion model aligns color, illumination, and texture statistics of synthetic images to the realistic style, using only a small real reference set; (2) Local Refinement -- a super-resolution diffusion model is used to facilitate fine-grained and photorealistic details for the small objects, such as human instances, preserving shape and boundary integrity; (3) Hallucination Removal -- a module that filters out human instances whose visual attributes do not align with real-world data to make the human appearance closer to the target distribution. Extensive experiments on public UAV Sim2Real detection benchmarks demonstrate that our methods significantly improve the detection accuracy compared to the non-transformed baselines. Specifically, our method achieves up to $+14.1$ improvement of mAP50 on Semantic-Drone benchmark. Ablation studies confirm the complementary roles of the global and local stages and highlight the importance of hierarchical alignment. The code is released at \href{this https URL}{this url}.
Abstract (translated)
训练目标检测器需要大量的、特定任务的标注数据，但在基于无人机的人类检测中，由于目标分布不断变化以及标记图像稀少，这一要求变得不切实际。作为解决方案，合成模拟器被用来生成有注释的数据，这大大降低了标注成本。然而，合成与真实图像之间的领域差距阻碍了模型在目标领域的有效应用。为此，我们引入了一种名为“粗到细层次对齐”（Coarse-to-Fine Hierarchical Alignment, CFHA）的三阶段扩散框架，旨在将合成数据转换为适用于基于无人机的人类检测，并缩小域间差异，同时保持原始标签信息不变。 CFHA明确地解耦了全局风格与局部内容领域的不一致，并通过三个模块来弥补这些差距：（1）全局样式迁移——一个扩散模型仅使用少量的真实参考集，就能将合成图像的颜色、光照和纹理统计特性对齐到真实样式的水平；（2）局部细化——一种超分辨率扩散模型被用来促进小物体（如人类实例）的细粒度和逼真细节，同时保持形状和边界的完整性；（3）幻觉移除——一个过滤模块将视觉属性与现实世界数据不一致的人类实例排除出去，使人类外观更接近目标分布。 在公共无人机Sim2Real检测基准上的广泛实验表明，我们的方法相比于未转换的基线显著提高了检测精度。具体而言，在Semantic-Drone基准上，我们的方法实现了高达+14.1 mAP50的改进。消融研究表明全局和局部阶段之间的互补作用，并强调了层次对齐的重要性。代码可在[此处](this https URL)获取。
URL
https://arxiv.org/abs/2512.13869
