Hybrid Contrast-Aware Fog Detection for Automotive Vision Systems

Modern vehicles are equipped with a wide range of Advanced Driver Assistance Systems (ADAS) that rely heavily on camerabased perception. Reliable visibility estimation – particularly under fog condition – remains a significant challenge. Accurate fog detection can enable proactive system responses, such as automatic activation of fog lights, and enhance operational safety.We present a contrast-aware anomaly detection framework for image-based fog detection. Our algorithm combines multi-scale Difference of Gaussians responses and Gaussian-weighted local Root Mean Squared contrast with a convolutional autoencoder. The model is trained exclusively on clear-weather imagery to learn the nominal scene distribution, and visibility degradation is detected as a reconstruction deviation from this learned representation. Evaluation on a separate test set containing clear and fog conditions demonstrates an AUC of 0.91, achieved without using fog samples during training. The framework provides a practical basis for camera-based visibility monitoring in automotive environments.