Road garbage recognition and cleanliness assessment are very important for intelligent cleaning of vehicles. However, widely used garbage detection methods cannot provide accurate information to assess cleanliness. Also, only a few contributions are available in the road cleanliness assessment, and they too mainly depend on the category and quantity of garbage. In this paper, deep supervision UNet++ (DUNet++) is proposed to solve the problem of road garbage classification and segmentation, which can directly impact the output of the category of garbage and the occupied ground area. A new, simple and accurate method is established to assess road cleanliness by combining the results of garbage segmentation. Compared with the cleanliness index (CI), the road cleanliness index based on semantic segmentation (CISS) not only considers the road area occupied by different types or categories of the garbage but also considers the difficulty of cleaning them. A road garbage segmentation dataset, which consists of four categories (stones, leaves, sand, and bottles), is collected to train the designed garbage segmentation model, and it has achieved considerable garbage segmentation improvement with an MIoU (Mean Intersection over Union) of 76.73±0.1176.73\pm 0.11. Especially when compared with the state-of-the-art methods, the model we designed has greatly improved the accuracy of garbage segmentation. Experimental results have shown that our road garbage segmentation and cleanliness assessment methods are relatively simple, and can provide accurate road cleanliness information for cleaning vehicles.

中文翻译：

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基于语义分割网络的清扫车道路垃圾分割与清洁度评估


道路垃圾识别和清洁度评估对于车辆的智能清洁非常重要。然而，广泛使用的垃圾检测方法无法提供准确的信息来评估清洁度。此外，道路清洁度评估的贡献有限，而且主要取决于垃圾的类别和数量。本文提出深度监督UNet++（DUNet++）来解决道路垃圾分类分割问题，它可以直接影响垃圾类别和占用地面面积的输出。结合垃圾分割结果，建立了一种简单、准确的道路清洁度评估新方法。与清洁度指数（CI）相比，基于语义分割的道路清洁度指数（CISS）不仅考虑了不同类型或类别的垃圾所占用的道路面积，而且还考虑了它们的清理难度。收集了由四类（石头、树叶、沙子和瓶子）组成的道路垃圾分割数据集来训练设计的垃圾分割模型，并取得了显着的垃圾分割改进，MIoU（联合平均交集）为76.73±0.1176.73\下午0.11。特别是与最先进的方法相比，我们设计的模型极大地提高了垃圾分割的准确性。实验结果表明，我们的道路垃圾分割和清洁度评估方法相对简单，可以为清扫车辆提供准确的道路清洁度信息。