Weather Resilient Object Detection: Focus On Foggy Weather Conditions

The “Domain Adaptive Object Detection for Autonomous Driving under Foggy Weather” is to improve autonomous vehicle object recognition under challenging fog conditions. Traditional models trained on clear-weather data are challenged by fog-induced domain shifts and reduced visibility. Using benchmark datasets like Cityscapes and Foggy Cityscapes, the study employs advanced domain adaptation techniques to solve this, including adversarial training, imagelevel and object-level adaptations, and synthetic data augmentation. These methods enhance the model's ability to accurately detect objects in difficult situations by reducing false negatives and ensuring consistent performance. This effort bridges the gap between clear and foggy weather conditions, contributing to safer and more dependable autonomous driving systems.