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.
翻译:汽车照相机,特别是环形照相机,往往会被泥土、水、雪等等污染。 对于更高水平的自动驾驶,有必要采用土壤检测算法,以触发自动清洁系统。在图像中进行本地化土壤检测对于控制清洁系统是必要的;同样有必要使未土壤地区部分功能化,同时降低对土壤地区的信心。尽管这可以通过一个语义分解任务来解决,但我们探索了一种针对低电源嵌入系统部署的更高效的解决方案。我们提出了一种新颖的方法,将每种土壤类型的区域直接在瓷砖中重新侵蚀。我们将此称为覆盖。拟议的方法比在陶瓷中学习主要土壤类要好,因为多种土壤类型通常在瓷砖中发生。它还具有处理粗微的多色图解析的优势,这将导致分解任务。拟议的土壤覆盖解析比等量级分解系统要快得多。我们还将它纳入一个物体检测和语义分解的多塔模型,我们称之为覆盖范围。拟议的方法比在陶瓷中学习一个主控层类类,因为多种土壤类型是常见的公开解算法,将用来鼓励公开解算数据。