  Object detection in urban scenarios is crucial for autonomous driving in
intelligent traffic systems. However, unlike conventional object detection
tasks, urban-scene images vary greatly in style. For example, images taken on
sunny days differ significantly from those taken on rainy days. Therefore,
models trained on sunny day images may not generalize well to rainy day images.
In this paper, we aim to solve the single-domain generalizable object detection
task in urban scenarios, meaning that a model trained on images from one
weather condition should be able to perform well on images from any other
weather conditions. To address this challenge, we propose a novel Double
AUGmentation (DoubleAUG) method that includes image- and feature-level
augmentation schemes. In the image-level augmentation, we consider the
variation in color information across different weather conditions and propose
a Color Perturbation (CP) method that randomly exchanges the RGB channels to
generate various images. In the feature-level augmentation, we propose to
utilize a Dual-Style Memory (DSM) to explore the diverse style information on
the entire dataset, further enhancing the model's generalization capability.
Extensive experiments demonstrate that our proposed method outperforms
state-of-the-art methods. Furthermore, ablation studies confirm the
effectiveness of each module in our proposed method. Moreover, our method is
plug-and-play and can be integrated into existing methods to further improve
model performance.
