Road curb detection is important for autonomous driving. It can be used to determine road boundaries to constrain vehicles on roads, so that potential accidents could be avoided. Most of the current methods detect road curbs online using vehicle-mounted sensors, such as cameras or 3-D Lidars. However, these methods usually suffer from severe occlusion issues. Especially in highly-dynamic traffic environments, most of the field of view is occupied by dynamic objects. To alleviate this issue, we detect road curbs offline using high-resolution aerial images in this paper. Moreover, the detected road curbs can be used to create high-definition (HD) maps for autonomous vehicles. Specifically, we first predict the pixel-wise segmentation map of road curbs, and then conduct a series of post-processing steps to extract the graph structure of road curbs. To tackle the disconnectivity issue in the segmentation maps, we propose an innovative connectivity-preserving loss (CP-loss) to improve the segmentation performance. The experimental results on a public dataset demonstrate the effectiveness of our proposed loss function. This paper is accompanied with a demonstration video and a supplementary document, which are available at \texttt{\url{https://sites.google.com/view/cp-loss}}.

翻译：对自主驾驶而言,道路阻力探测很重要,可以用来确定道路界限,以限制道路上的车辆,从而避免潜在的事故。目前大多数方法都使用车辆架设的传感器(如照相机或3D利达尔)在线探测道路阻力。然而,这些方法通常面临严重的隔热问题。特别是在高度动态的交通环境中,大多数视野领域都为动态物体所占据。为了缓解这一问题,我们用本文中的高清晰度空中图像探测道路阻力离线,从而避免潜在的事故。此外,所探测到的道路阻力可用于为自主车辆绘制高清晰度(HD)地图。具体地说,我们首先预测道路阻力路图,然后进行一系列后处理步骤,以提取路段的图形结构。为了解决路段图中的断裂性问题,我们建议采用创新的连接-保留损失(CP-损失)来改善断断层性性性功能。公共数据集的实验结果显示了我们拟议的损失功能的有效性。本文附有演示录象和补充文件,可在{httpstor/commlexloss。