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.

翻译：环形摄像头上人工土壤说明是一项非常艰巨和昂贵的任务。 诸如水滴或泥土颗粒等各种土壤类别的界限不明确,通常导致批注质量的巨大差异。 结果, 以如此缺乏注释性的数据培训的模型远非最佳。 在本文中, 我们侧重于通过假标签驱动的混合模型处理这种吵闹的注释, 从而使我们能够迅速发现有问题的注释, 在多数情况下, 也能够充分修补这些说明。 我们在吵闹和精细的标签上训练土壤分解模型, 并使用精细的注释显示显著的改进。 它还表明, 能够有效地完善成本较低的粗略说明。