将汽车摄像头污损检测视为语义分割任务，提升ADAS系统鲁棒性。
Soiling detection for Advanced Driver Assistance Systems
- 把污损检测当作语义分割问题处理，比传统分块分类更精准。
- 新数据子集仅用原数据一小部分，训练时间缩短且性能相当。
- 揭示Woodscape数据集存在标注不精确和数据泄露问题，适合自动驾驶研究者参考。
汽车摄像头的污损检测是提升高级驾驶辅助系统鲁棒性的关键环节，尤其在应对天气、灰尘等外部条件时。本文将污损检测建模为语义分割任务，对主流分割方法进行综合对比，证明其在性能上优于传统的图像块分类方法。同时，我们对Woodscape数据集进行了深入分析，发现原数据集存在数据泄露和标注不准确的问题。为此，我们构建了一个新的小规模数据子集，尽管规模远小于原数据，但足以让分割模型在更短时间内达到相近性能。所有代码与数据划分已公开于https://github.com/filipberanek/woodscape_revision。
原文摘要 · Abstract (English)
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.
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