FogTTA: Online Test-Time Adaptation for Robust Transformer-based Object Detection in Foggy Weather

FogTTA is an online test-time adaptation framework that enhances Transformer-based object detectors, specifically RF-DETR, for foggy weather conditions. It operates without source data or retraining by using a teacher-student architecture: the deployed model generates pseudo labels from weakly augmented inputs, which are refined and used to train a student model on strongly augmented samples with Varifocal loss. The teacher model is updated via exponential moving averaging, enabling real-time, stable adaptation and improved detection accuracy in foggy environments.

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