DA-RAW: Domain Adaptive Object Detection for Real-World Adverse Weather Conditions

arXiv:2309.08152v2 Announce Type: replace
Abstract: 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 …

abstract arxiv cs.cv cs.ro deep learning detection detection methods detectors domain domain adaptation object performance rain raw robust snow success type unsupervised weather world