Hybrid Contrast-Aware Fog Detection for Automotive Vision Systems
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Date
2026
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
Modern vehicles are equipped with a wide range of Advanced Driver Assistance Systems (ADAS) that rely heavily on camera-based 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.
Description
CCS Concepts: Computing methodologies → Computer vision; Neural networks
@inproceedings{10.2312:egp.20261005,
booktitle = {Eurographics 2026 - Posters},
editor = {Gerrits, Tim and Teschner, Matthias},
title = {{Hybrid Contrast-Aware Fog Detection for Automotive Vision Systems}},
author = {Procházková, Jana and Mikuláček, Pavel and Štarha, Pavel },
year = {2026},
publisher = {The Eurographics Association},
ISSN = {1017-4656},
ISBN = {978-3-03868-300-1},
DOI = {10.2312/egp.20261005}
}