Despite the success of deep learning-based object detection methods in recent years, it is still challenging to make the object detector reliable in adverse weather conditions such as rain and snow. For the robust performance of object detectors, unsupervised domain adaptation has been utilized to adapt the detection network trained on clear weather images to adverse weather images. While previous methods do not explicitly address weather corruption during adaptation, the domain gap between clear and adverse weather can be decomposed into two factors with distinct characteristics: a style gap and a weather gap. In this paper, we present an unsupervised domain adaptation framework for object detection that can more effectively adapt to real-world environments with adverse weather conditions by addressing these two gaps separately. Our method resolves the style gap by concentrating on style-related information of high-level features using an attention module. Using self-supervised contrastive learning, our framework then reduces the weather gap and acquires instance features that are robust to weather corruption. Extensive experiments demonstrate that our method outperforms other methods for object detection in adverse weather conditions.

翻译：尽管基于深度学习的目标检测方法近年来取得了成功，但在雨雪等恶劣天气条件下仍难以保证检测器的可靠性。为实现检测器的鲁棒性能，无监督领域自适应被用于将基于晴朗天气图像训练的检测网络适配至恶劣天气图像。现有方法在自适应过程中并未显式处理天气退化问题，而晴朗天气与恶劣天气之间的领域差异可分解为两种具有不同特性的因子：风格差异与天气差异。本文提出一种面向目标检测的无监督领域自适应框架，通过分别处理这两类差异，能更有效地适应存在恶劣天气条件的真实环境。该方法通过注意力模块聚焦高层特征的风格相关信息以解决风格差异，并利用自监督对比学习缩小天气差异，从而获取对天气退化具有鲁棒性的实例特征。大量实验证明，本方法在恶劣天气条件下的目标检测性能优于其他方法。