Road-boundary detection is important for autonomous driving. It can be used to constrain autonomous vehicles running on road areas to ensure driving safety. Compared with online road-boundary detection using on-vehicle cameras/Lidars, offline detection using aerial images could alleviate the severe occlusion issue. Moreover, the offline detection results can be directly employed to annotate high-definition (HD) maps. In recent years, deep-learning technologies have been used in offline detection. But there still lacks a publicly available dataset for this task, which hinders the research progress in this area. So in this paper, we propose a new benchmark dataset, named \textit{Topo-boundary}, for offline topological road-boundary detection. The dataset contains 25,295 $1000\times1000$-sized 4-channel aerial images. Each image is provided with 8 training labels for different sub-tasks. We also design a new entropy-based metric for connectivity evaluation, which could better handle noises or outliers. We implement and evaluate 3 segmentation-based baselines and 5 graph-based baselines using the dataset. We also propose a new imitation-learning-based baseline which is enhanced from our previous work. The superiority of our enhancement is demonstrated from the comparison. The dataset and our-implemented code for the baselines are available at \texttt{\url{https://tonyxuqaq.github.io/Topo-boundary/}}.
翻译:对自主驾驶来说,路边探测很重要。 它可用于限制在路区运行的自主车辆,以确保驾驶安全。 与使用车辆摄像机/Lidars 进行的在线路边探测相比, 使用空中图像进行的离线探测可以缓解严重的封闭性问题。 此外, 离线探测结果可以直接用于高清晰度(HD)地图的注释性高清晰度( HD) 地图。 近年来, 已在离线检测中使用了深学习技术。 但是, 仍然缺乏用于这项任务的公开数据集, 从而阻碍了这一领域的研究进展。 因此, 在本文中, 我们提出一个新的基准数据集, 名为\ textit{Topo- marterary}, 用于进行离线式表层检测。 该数据集包含25, 295, 1 000 time, 100美元大小的4个通道空中图像。 每张图像都配有8个培训标签,用于不同子塔的检测。 我们还设计了一个新的基于英特罗基的连接评价基准, 它可以更好地处理噪声或离子。 因此, 我们实施和评估基于3个分区的基线和5个基于图表的基线, 我们的升级的升级的升级的基线, 也是从先前的数据的升级的升级的升级的基础。