SASG: Semantic-Aware Salient Guidance for Day-to-Night Domain Adaptive Object Detection

Detecting objects in nighttime low-light and weak-illumination scenarios presents a persistent challenge, as detectors trained on daytime well-illuminated data exhibit significant performance degradation on nighttime data due to low visibility. Previous methods mitigate day-to-night domain adaptation problems by leveraging teacher-student framework and image enhancement techniques. However, these methods uniformly process all foreground and background regions, resulting in an over-reliance on background scenes and struggling to learn effective features for typically sparse and small objects. To address this challenge, we propose a Semantic-Aware Salient Guidance day-to-night domain adaptive object detection framework (SASG). We first design an object Semantic Saliency Guidance (SSG) module, which leverages object semantic information to saliency guide the detection network to focus on the object regions (i.e., pedestrians, traffic signs) in the image, reducing attention to background scenes (i.e., roads, sky). Next, we introduce a compact Image Relighting Network (IRN) to narrow the intensity distribution gap between daytime and nighttime images, reducing the detector sensitivity to illumination variations. Extensive experiments demonstrate that our approach achieves superior nighttime low-light and weak-illumination generalization performance on the BDD100K and SHIFT datasets, outperforming the state-of-the-art methods by 2.5% and 1.2%, respectively.