CLP: A Real-World Dataset of Contaminated Lens Protectors for Robust Semantic Segmentation

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.