Abstract

Semantic segmentation in foggy scenes is crucial for autonomous driving systems, yet acquiring annotated real-world foggy data is highly costly. Existing unsupervised domain adaptation methods typically adopt a self-training strategy, adapting models trained under clear-weather conditions to unlabeled foggy target domains and constructing supervision signals via pseudo-labels. However, these methods mainly focus on improving the reliability of target-domain supervision while paying insufficient attention to class confusion caused by the degradation of class discriminability. In fact, the performance degradation of self-training in foggy scenarios is not caused by a single factor, but is jointly affected by unreliable supervision signals and reduced class discriminability. To address these issues, this paper proposes a reliable pseudo-labeling and confusion calibration framework for foggy-scene semantic segmentation, termed RPCC. Specifically, dynamic energy-guided pseudo-labeling (DEPL) models the reliability of target-domain predictions using energy scores, thereby improving the reliability of target-domain supervision signals. Furthermore, the reliable-region class confusion calibration (RCC) module models and calibrates semantic class relationships in target-domain predictions based on reliable pseudo-label supervision, thereby suppressing class confusion and enhancing semantic boundary clarity. Experimental results demonstrate that RPCC outperforms existing methods on multiple real-world foggy-scene datasets and shows favorable generalization to other adverse weather conditions.

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