# Soiling detection for Advanced Driver Assistance Systems

Source: [Soiling detection for Advanced Driver Assistance Systems](<https://www.sota2.com/research/paper/6916b7920052cac1dc65e07c>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-11-15T05:02:48.629Z

Authors: Filip Ber\\'anek, V\\'aclav Divi\\v{s}, Ivan Gruber

Published: 2025-11-12T21:02:21.000Z

## Abstract

Soiling detection for automotive cameras is a crucial part of advanced driver assistance systems to make them more robust to external conditions like weather, dust, etc. In this paper, we regard the soiling detection as a semantic segmentation problem. We provide a comprehensive comparison of popular segmentation methods and show their superiority in performance while comparing them to tile-level classification approaches. Moreover, we present an extensive analysis of the Woodscape dataset showing that the original dataset contains a data-leakage and imprecise annotations. To address these problems, we create a new data subset, which, despite being much smaller, provides enough information for the segmentation method to reach comparable results in a much shorter time. All our codes and dataset splits are available at https://github.com/filipberanek/woodscape\_revision.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2511.09740>)

[PDF](<https://arxiv.org/pdf/2511.09740.pdf>)