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Automotive cameras, particularly surround-view cameras, tend to get soiled bymud, water, snow, etc. For higher levels of autonomous driving, it is necessaryto have a soiling detection algorithm which will trigger an automatic cleaningsystem. Localized detection of soiling in an image is necessary to control thecleaning system. It is also necessary to enable partial functionality inunsoiled areas while reducing confidence in soiled areas. Although this can besolved using a semantic segmentation task, we explore a more efficient solutiontargeting deployment in low power embedded system. We propose a novel method toregress the area of each soiling type within a tile directly. We refer to thisas coverage. The proposed approach is better than learning the dominant classin a tile as multiple soiling types occur within a tile commonly. It also hasthe advantage of dealing with coarse polygon annotation, which will cause thesegmentation task. The proposed soiling coverage decoder is an order ofmagnitude faster than an equivalent segmentation decoder. We also integrated itinto an object detection and semantic segmentation multi-task model using anasynchronous back-propagation algorithm. A portion of the dataset used will bereleased publicly as part of our WoodScape dataset to encourage furtherresearch.

TiledSoilingNet: Tile-level Soiling Detection on Automotive Surround-view Cameras Using Coverage Metric