SSCL: Semi-Supervised Comprehensive Learning for Nighttime Semantic Segmentation

Ensuring resilient semantic segmentation under diverse outdoor conditions is vital for autonomous driving. However, nighttime segmentation remains underdeveloped compared to its daytime counterpart due to poor illumination and scarce annotated data, posing a significant challenge for night-scene understanding. Most current approaches mainly rely on domain adaptation technologies to transfer segmentation models trained on daytime scenes. However, the substantial distinctions between daytime and nighttime domains often hinder effective adaptation. To address this challenge, we leverage the implicit comprehensive information within nighttime data to enhance semantic segmentation through a semi-supervised approach. Specifically, we introduce a Semi-Supervised Comprehensive Learning (SSCL) approach, which is a unified, closed-loop learning architecture composed of three mutually reinforcing correction mechanisms: (1) unsupervised interactive correction to mitigate the risk of erroneous label propagation by leveraging complementary learning abilities; (2) unsupervised reinforcement correction, which enhances the model’s adaptability by promoting diverse learning on high-uncertainty regions through entropy-guided perturbation; (3) supervised standard correction to ensure alignment with known standards by anchoring the system to reference answers. SSCL is the first semi-supervised algorithm that jointly exploits structural diversity, uncertainty-aware supervision, and closed-loop correction to fully harness the latent potential of unlabeled nighttime data. Extensive experiments on NightCity, Dark Zurich and Nighttime Driving datasets demonstrate that SSCL achieves state-of-the-art performance in nighttime semantic segmentation.