Synthetic coherent data augmentation for adverse weather conditions in surveillance systems

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.