AI-Based Image Quality and Lens Defect Analysis in Autonomous Driving: A Framework with U-Net-Based Soiling Detection

Ensuring reliable camera vision in autonomous driving systems requires continuous monitoring of image quality and lens integrity. External contaminants such as dust, raindrops, and mud, as well as permanent defects like cracks or scratches, can severely degrade visual perception and compromise safety-critical tasks such as lane detection, obstacle recognition, and path planning. This paper presents an AI-based framework that integrates image quality assessment (IQA) and lens defect analysis to enhance the robustness of camera-based perception systems in autonomous vehicles. Building on previous conceptual work in safety-aware lens defect detection, the proposed framework introduces a dual-layer architecture that combines real-time IQA monitoring with deep learning-based soiling segmentation. As an initial experimental validation, a U-Net model was trained on the WoodScape Soiling dataset to perform pixel-level detection of lens contamination. The model achieved an average Intersection-over-Union (IoU) of 0.6163, a Dice coefficient of 0.7626, and a recall of 0.9780, confirming its effectiveness in identifying soiled regions under diverse lighting and environmental conditions. Beyond the experiment, this framework outlines pathways for future integration of semantic segmentation, anomaly detection, and safety-driven decision policies aligned with ISO 26262 and ISO 21448 standards. By bridging conceptual modeling with experimental evidence, this study establishes a foundation for intelligent camera health monitoring and fault-tolerant perception in autonomous driving. The presented results demonstrate that AI-based image quality and defect assessment can significantly improve system reliability, supporting safer and more adaptive driving under real-world conditions.