悪天候下の実世界向けドメイン適応物体検出
DA-RAW: Domain Adaptive Object Detection for Real-World Adverse Weather Conditions
晴天画像で学習した物体検出器を雨天・雪などの悪天候環境に適応させるため、スタイル差と天候差を分離して対処する教師なしドメイン適応フレームワークを提案した。
著者: Minsik Jeon, Junwon Seo, Jihong Min
分類: cs.CV, cs.RO
原文アブストラクト
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.
