  Manual annotation of soiling on surround view cameras is a very challenging
and expensive task. The unclear boundary for various soiling categories like
water drops or mud particles usually results in a large variance in the
annotation quality. As a result, the models trained on such poorly annotated
data are far from being optimal. In this paper, we focus on handling such noisy
annotations via pseudo-label driven ensemble model which allow us to quickly
spot problematic annotations and in most cases also sufficiently fixing them.
We train a soiling segmentation model on both noisy and refined labels and
demonstrate significant improvements using the refined annotations. It also
illustrates that it is possible to effectively refine lower cost coarse
annotations.
