  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.
