GS-Mix: Simple Pixel-level Mixing for Single-Domain Generalized Object Detection

Pixel-level data augmentation has proven to be an effective strategy for single-domain generalized object detection (S-DGOD). However, existing methods struggle to balance augmentation intensity with the preservation of object-relevant structures, thereby restricting strong perturbations for diverse coverage. To address this, we analyze the key factors and influence mechanism in S-DGOD domain shift, and accordingly propose a vicinal distribution that synthesizes samples with domain shift perturbations (noise and texture) while robustly preserving structure. To instantiate this distribution, we construct a simple yet effective pixel-level intra-domain mixing framework, termed Global-context Structure-texture Mix (GS-Mix). Specifically, global-context mixing (GCM) yields diverse noise perturbations through linear interpolation between source images, and structure-texture mixing (STM) uses structure masks to apply strong texture transformations. These modules cover global-to-local strong augmentations and avoid destroying structural information. GS-Mix can be easily taken as a plug-and-play data augmentation module and introduces almost no extra costs in the training phase. Experimental results on real-world domain generalization benchmarks, including adverse weather, underwater, and medical imaging, show the superior performance and easy scalability of our GS-Mix compared to existing methods.