  Automotive cameras, particularly surround-view cameras, tend to get soiled by
mud, water, snow, etc. For higher levels of autonomous driving, it is necessary
to have a soiling detection algorithm which will trigger an automatic cleaning
system. Localized detection of soiling in an image is necessary to control the
cleaning system. It is also necessary to enable partial functionality in
unsoiled areas while reducing confidence in soiled areas. Although this can be
solved using a semantic segmentation task, we explore a more efficient solution
targeting deployment in low power embedded system. We propose a novel method to
regress the area of each soiling type within a tile directly. We refer to this
as coverage. The proposed approach is better than learning the dominant class
in a tile as multiple soiling types occur within a tile commonly. It also has
the advantage of dealing with coarse polygon annotation, which will cause the
segmentation task. The proposed soiling coverage decoder is an order of
magnitude faster than an equivalent segmentation decoder. We also integrated it
into an object detection and semantic segmentation multi-task model using an
asynchronous back-propagation algorithm. A portion of the dataset used will be
released publicly as part of our WoodScape dataset to encourage further
research.
