Cameras play a crucial role in modern driver assistance systems and are an essential part of the sensor technology for automated driving. The quality of images captured by in-vehicle cameras highly influences the performance of visual perception systems. This paper presents a feature-based algorithm to detect certain effects that can degrade image quality in automotive applications. The algorithm is based on an intelligent selection of significant features. Due to the small number of features, the algorithm performs well even with small data sets. Experiments with different data sets show that the algorithm can detect soiling adhering to camera lenses and classify different types of image degradation.

翻译：摄像头在现代驾驶员辅助系统中扮演着关键角色，且是自动驾驶传感器技术的重要组成部分。车载摄像头捕获的图像质量会显著影响视觉感知系统的性能。本文提出了一种基于特征的算法，用于检测汽车应用中可能降低图像质量的特定效应。该算法基于对显著特征的智能筛选。由于特征数量较少，该算法即使在数据量较小时仍能表现出良好性能。针对不同数据集的实验表明，该算法能够检测附着于摄像头镜头的污渍，并对不同类型的图像退化进行分类。