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.

中文翻译：

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TiledSoilingNet：使用覆盖指标对汽车全景摄像头进行瓷砖级污染检测

汽车摄像头，尤其是环视摄像头，容易被泥、水、雪等弄脏。对于更高级别的自动驾驶，需要有一个污染检测算法，它会触发自动清洁系统。图像中污渍的局部检测对于控制清洁系统是必要的。还必须在未污染区域启用部分功能，同时降低对污染区域的置信度。虽然这可以使用语义分割任务来解决，但我们探索了一种更有效的解决方案，目标是在低功耗嵌入式系统中进行部署。我们提出了一种新方法来直接回归瓷砖内每种污染类型的面积。我们将此称为覆盖率。所提出的方法比学习瓷砖中的主导类要好，因为瓷砖内通常会出现多种污染类型。它还具有处理粗多边形注释的优点，这将导致分割任务。所提出的污染覆盖解码器比等效的分割解码器快一个数量级。我们还使用异步反向传播算法将其集成到对象检测和语义分割多任务模型中。使用的部分数据集将作为 WoodScape 数据集的一部分公开发布，以鼓励进一步研究。