Foggy Drone Teacher: Domain Adaptive Drone Detection Under Foggy Conditions

With the growing use of drones, efficient detection algorithms are crucial, especially under adverse weather conditions. Most existing drone detection algorithms perform well only in clear weather, resulting in significant performance drops in foggy conditions. This study focuses on improving drone detection in foggy environments using the Mean Teacher framework for domain adaptation. The Mean Teacher framework’s performance relies on the quality of the teacher model’s pseudo-labels. To enhance the quality of the pseudo-labels from the teacher model, we introduce Foggy Drone Teacher (FDT), which includes three key components: (1) Adaptive Style and Context Augmentation to reduce domain shift and improve pseudo-label quality; (2) Simplified Domain Alignment with a novel adversarial strategy to boost domain adaptation; and (3) Progressive Domain Adaptation Training, a two-stage process that helps the teacher model produce more stable and accurate pseudo-labels. In addition, owing to the lack of publicly available data, we created Foggy Drone Dataset (FDD) to support this research. Extensive experiments show that our model achieves a 21.1-point increase in AP0.5 compared to the baseline and outperforms state-of-the-art models. This method significantly improves drone detection accuracy in foggy conditions.