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Related Experiment Videos

A Static-to-Temporal Framework for Interpretable Camera Lens Soiling Severity Estimation in Autonomous Driving.

Fan Yang1, Xingyu Duan2, Fan Li1

  • 1State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle, Hunan University, Changsha 410082, China.

Sensors (Basel, Switzerland)
|June 12, 2026
PubMed
Summary
This summary is machine-generated.

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Relative Motion Analysis using Rotating Axes

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Estimating camera lens soiling severity is crucial for autonomous driving safety. This study introduces a novel static-to-temporal framework that improves accuracy and temporal stability, enhancing perception system reliability.

Area of Science:

  • Computer Vision
  • Autonomous Driving Systems
  • Sensor Technology

Background:

  • Camera lens soiling significantly impairs visual perception in autonomous vehicles, necessitating accurate severity estimation for safety.
  • Existing methods lack robustness due to insufficient consideration of opacity, spatial importance, and temporal dynamics in video streams.

Purpose of the Study:

  • To develop a robust static-to-temporal framework for camera-soiling severity estimation.
  • To address limitations in current methods regarding opacity, spatial importance, and temporal stability.
  • To enhance camera-health monitoring and downstream perception safety in autonomous driving.

Main Methods:

  • Proposed a Structured Dual-Head Static Model (SDSM) for joint tile-level and image-level soiling prediction with an explicit Structured Severity Score.
Keywords:
autonomous drivingcamera lens soilingdual-head learningseverity estimationstable diffusiontemporal stabilization

Related Experiment Videos

  • Developed a Two-Stage Stable Diffusion (TS-SD) pipeline to generate SD-Seq data for mechanism supervision, overcoming real temporal data scarcity.
  • Introduced a structure-constrained adaptive EMA Module to enhance temporal stability while maintaining the single-frame severity scale.

    • Main Results:

    • Achieved strong cross-domain severity estimation performance on WoodScape, External Test, and OccNuScenes-Dirt datasets.
    • Attained a cluster-level Spearman correlation of 0.7876 on the External Test dataset.
    • The temporal module reduced Jitter (MAD) by 51.5%, significantly improving temporal stability.

    Conclusions:

    • The proposed framework offers an interpretable, cross-domain, and deployment-friendly solution for camera-soiling assessment.
    • This approach enhances the reliability of visual perception for autonomous driving systems.
    • The method effectively addresses the challenges of opacity, spatial importance, and temporal dynamics in soiling estimation.