Bridging the synthetic-to-real domain gap with network dissection analysis

Adverse weather, such as heavy fog, rain, or snowstorm reduces the performance of object detection in surveillance systems. Collecting diverse real-world data for these rare events is difficult, limiting model robustness. To address this, we propose a generative-AI-based coherent data augmentation framework that synthetically simulates realistic weather effects on surveillance footage. Using image-to-video generation, prompt engineering, and cascading pipelines, our method produces photorealistic data for benchmarking and developing detectors capable of sustaining accuracy under challenging environmental conditions.