Reliable-Teacher: Uncertainty-Guided Collaborative Learning for Nighttime Object Detection

Nighttime object detection presents significant challenges due to the scarcity of large-scale, high-quality annotations across diverse nighttime scenarios. To circumvent the need for manual nighttime image annotation, researchers have explored Unsupervised Domain Adaptive Object Detection (UDA-OD), which transfers knowledge from labeled daytime datasets to unlabeled nighttime data through pseudo-labeling. While existing approaches have shown promising results, their effectiveness remains limited by the low quality of pseudo labels, restricting model adaptation to nighttime conditions. To address these limitations, we propose Reliable-Teacher, a novel mutual-learning framework that comprehensively leverages target domain knowledge through Uncertainty-Guided Collaborative Learning. Specifically, our approach consists of three key components: 1) A Collaborative Pseudo-Label Construction module that intelligently integrates reliable Teacher-generated pseudo-labels into Student proposals, significantly enhancing pseudo-label quality; 2) An Uncertainty-Guided Consistency Reasoning module that enforces inter-category consistency between Teacher and Student predictions at both anchor and bounding box levels; 3) A Reliability-Weighted Classification Loss that minimizes the influence of unreliable predictions to further enhance uncertainty-guided learning. Extensive experiments demonstrate that Reliable-Teacher significantly outperforms state-of-the-art methods, achieving performance gain of up to 3.1%, 2.2% and 1.7% mAP on BDD100K [1], SHIFT [2], and VisDrone [3] benchmarks, respectively. Upon acceptance, our code will be released to facilitate further research in this domain.