  With the rise of autonomous vehicles and advanced driver-assistance systems
(ADAS), ensuring reliable object detection in all weather conditions is crucial
for safety and efficiency. Adverse weather like snow, rain, and fog presents
major challenges for current detection systems, often resulting in failures and
potential safety risks. This paper introduces a novel framework and pipeline
designed to improve object detection under such conditions, focusing on traffic
signal detection where traditional methods often fail due to domain shifts
caused by adverse weather. We provide a comprehensive analysis of the
limitations of existing techniques. Our proposed pipeline significantly
enhances detection accuracy in snow, rain, and fog. Results show a 40.8%
improvement in average IoU and F1 scores compared to naive fine-tuning and a
22.4% performance increase in domain shift scenarios, such as training on
artificial snow and testing on rain images.
