交通标志等道路设施的空间位置信息是城市三维建模的基本要素之一,也是道路设施养护管理的必要内容。为此,提出一种基于移动测量数据的小型交通标志自动提取方案,基于改进的卷积神经网络SlimNet模型对全景影像上的小交标进行检测,提出一种基于深度图的目标三维空间地理定位方法,并采用以中心点为准的距离判断法提取目标的对角线。选取三类小型交通标志的实测数据对所提方法进行验证分析。实验结果表明,SlimNet模型的平均正确率相比经典的VGG16(Visual Geometry Group 16)模型有4.2个百分点的提升。采用提出的地理定位和矢量化方案,三类目标在实验区的召回率和精确率均达到86%以上,证明整体方案的有效可行性。该方法为城市多类目标的精确三维空间地理定位提供新思路。

Information on the spatial location of road facilities such as traffic signs is one of the basic element of urban three-dimensional (3D) modeling, and it is also an essential part of road facility maintenance and management. To this end, an automatic extraction scheme for small traffic signs based on mobile measurement data is proposed herein. Based on the improved convolutional neural network SlimNet model, small cross-marks on panoramic images are detected, and a 3D target geolocation based on depth maps is proposed. A distance assessment method based on the center point is used to extract the diagonal of the target. Measured data of the three types of small traffic signs are selected to verify and analyze the proposed method. Experiment results show that the average accuracy of the SlimNet model is 4.2 percentage higher than that of the classic VGG16 (Visual Geometry Group 16) model. Using the proposed geographic positioning and vectorization scheme, the recall rate and accuracy rate of the three types of targets in the experimental area reached over 86%, proving the effective feasibility of the overall scheme. Furthermore, the proposed method provides a new idea for an accurate 3D spatial geolocalization of urban multi-class targets.