  Single-domain generalization (S-DG) aims to generalize a model to unseen
environments with a single-source domain. However, most S-DG approaches have
been conducted in the field of classification. When these approaches are
applied to object detection, the semantic features of some objects can be
damaged, which can lead to imprecise object localization and misclassification.
To address these problems, we propose an object-aware domain generalization
(OA-DG) method for single-domain generalization in object detection. Our method
consists of data augmentation and training strategy, which are called OA-Mix
and OA-Loss, respectively. OA-Mix generates multi-domain data with multi-level
transformation and object-aware mixing strategy. OA-Loss enables models to
learn domain-invariant representations for objects and backgrounds from the
original and OA-Mixed images. Our proposed method outperforms state-of-the-art
works on standard benchmarks. Our code is available at
https://github.com/WoojuLee24/OA-DG.
