A Sample-Mixing Unsupervised Domain Adaptation Framework for Object Detection

Object detection across diverse real-world environments poses significant challenges when labeled data is scarce, particularly in privacy-sensitive scenarios such as healthcare, where data annotation is costly and access to labeled data is often restricted. In healthcare settings, robust human detection systems can enable critical applications such as patient monitoring, rehabilitation assessment, and clinical workflow optimization, yet their deployment is hindered by domain shift when models are applied to new environments. To address these challenges, this paper presents a novel unsupervised domain adaptation approach for object detection that effectively bridges the domain gap between source and target domains. Our method introduces a multi-scale sample mixing strategy guided by region-level detection confidence estimation, with the core innovation lying in our selective region mixing mechanism and multi-scale uncertainty estimation. This approach enables accurate detection of objects of varying sizes without requiring target domain annotations. Extensive experiments demonstrate that our method adapts robustly across diverse environments, offering a practical solution for deploying AI systems in scenarios where privacy concerns and annotation costs limit the availability of labeled data.