Zhang Kai, Yuan Xia, Xu Jiachen, Wang Kaiyang, Wu Shiwei, Zhao Chunxia
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210000, China.
Software Development Department, Dahua Technology, Hangzhou, 310000, China.
Sci Data. 2025 Jan 28;12(1):164. doi: 10.1038/s41597-025-04457-3.
Travelable area boundaries not only constrain the movement of field robots but also indicate alternative guiding routes for dynamic objects. Publicly available road boundary datasets have outlined boundaries by binary segmentation labels. However, hard post-processes have to be done to extract from detected boundaries further semantics including the shapes of the boundaries and guiding routes, which poses challenges to a real-time visual navigation system without detailed prior maps. In addition, boundary detectors suffer from insufficient data collected from complex roads with severe occlusion and of different shapes. In this paper, a travelable area boundary dataset is semi-automatically built. 82.05% of the data is collected from bends, crossroads, T-shape roads and other irregular roads. Novel guiding semantics labels, shape labels and scene complexity labels are assigned to boundaries. With the support of the new dataset, travelable area boundary detectors could be trained, evaluated and fairly compared. The dataset can also be used to train, evaluate or test detectors for the road boundary detection task.
可通行区域边界不仅限制了野外机器人的移动，还为动态物体指明了可供选择的引导路线。公开可用的道路边界数据集通过二进制分割标签勾勒出边界。然而，必须进行复杂的后处理，以便从检测到的边界中提取更多语义信息，包括边界形状和引导路线，这对没有详细先验地图的实时视觉导航系统构成了挑战。此外，边界检测器还面临着从复杂道路（严重遮挡且形状各异）收集的数据不足的问题。在本文中，我们半自动构建了一个可通行区域边界数据集。82.05%的数据是从弯道、十字路口、丁字路口和其他不规则道路收集的。为边界分配了新颖的引导语义标签、形状标签和场景复杂度标签。在新数据集的支持下，可以对可通行区域边界检测器进行训练、评估和公平比较。该数据集还可用于训练、评估或测试道路边界检测任务的检测器。
