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

Engineering notes

Key topics: autonomous driving, nuscenes, deployment, perception, prediction. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Camera lens soiling can severely degrade visual perception in autonomous driving, making reliable soiling severity estimation essential for camera-health monitoring and downstream perception safety. However, existing methods mainly rely on area-based indicators or frame-wise predictions, which insufficiently account for opacity differences, spatial importance, and temporal stability in continuous video streams. To address these limitations, this paper proposes a static-to-temporal soiling framework for camera-soiling severity estimation. First, we propose a Structured Dual-Head Static Model (SDSM) that jointly predicts tile-level four-class soiling distributions and an image-level severity score. The model is coupled with an explicit Structured Severity Score that aggregates local predictions through opacity-aware, spatial, and dominance-related components. Second, to alleviate the scarcity of real temporal soiling data, we construct a Two-Stage Stable Diffusion (TS-SD) pipeline and use the resulting SD-Seq data as mechanism supervision for temporal learning rather than direct single-frame strong supervision. Finally, we introduce a structure-constrained adaptive EMA Module to improve temporal stability while preserving the original single-frame severity scale. Experiments on WoodScape, External Test, and OccNuScenes-Dirt show strong cross-domain severity estimation performance, including a cluster-level Spearman correlation of 0.7876 on External Test. The temporal module further reduces Jitter (MAD) by 51.5%. These results demonstrate an interpretable, cross-domain, and deployment-friendly solution for camera-soiling assessment.

Links and sources

Comments