CLP：面向鲁棒语义分割的真实世界受污染镜头保护罩数据集
CLP: A Real-World Dataset of Contaminated Lens Protectors for Robust Semantic Segmentation
为镜头保护罩污染这一真实退化提供带密集语义分割掩码与对齐复原目标的基准，可直接用于评测域泛化、基础模型微调以及复原-分割联合流水线在有限数据下的表现。
原文依据 we introduce the CLP (Contaminated Lens Protector) dataset, a real-world benchmark designed to evaluate perception performance
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论自主系统在真实环境中的可靠性主要取决于其视觉感知的鲁棒性。尽管近期研究在视觉退化处理方面取得了进展，但附着在相机镜头上的物理污染物——如泥浆、水滴和冷凝水——在很大程度上仍未被充分探索。为此，我们提出了 CLP（受污染镜头保护罩）数据集，这是一个真实世界基准，旨在评估真实镜头保护罩污染条件下的感知性能。CLP 数据集提供了涵盖多种污染类型和不同镜头到保护罩距离的退化图像，并附带密集语义分割掩码和对齐的复原目标。该数据集使得在与真实自主系统所遇条件高度接近的情形下开展鲁棒的语义分割与复原研究成为可能。实验分析了在数据有限条件下提升污染场景感知的策略，凸显了域泛化、基础模型、数据规模以及复原-分割联合流水线的重要性。
英文原文摘要 1087 字符
The reliability of autonomous systems in real-world environments is mainly dependent on the robustness of their visual perception.Although recent studies have advanced the handling of visual degradations, physical contaminants that adhere to the camera lens--such as mud, water droplets, and condensation--remain largely underexplored.To this end, we introduce the CLP (Contaminated Lens Protector) dataset, a real-world benchmark designed to evaluate perception performance under realistic lens-protector contamination.The CLP dataset offers degraded images across multiple types of contamination and various lens-to-protector distances, along with dense semantic segmentation masks and aligned restoration targets.This dataset enables robust segmentation and restoration studies in conditions that closely match those encountered by real-world autonomous systems.Experiments analyze strategies to improve perception under contamination with limited data, highlighting the importance of domain generalization, foundation models, data scale, and joint restoration-segmentation pipelines.