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Visualization of 3D cable between utility poles obtained from laser scanning point clouds: a case study

机译:从激光扫描点云获得的公用事业杆之间的3D电缆可视化:案例研究

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摘要

We can automate inspection work of infrastructure facilities by analyzing the characteristics of 3D structure information obtained through 3D structure visualization using a point cloud. The safety level of equipment can then be diagnosed quantitatively. In this paper, we investigate the modeling of wire structures such as overhead communication cables between utility poles, which are close to the ground, have many obstructions, and have a complex structure. We evaluate the accuracy of cable models and compare them to the correct model. We use three modeling methods: a machine-learning method based on the extruded surface of a point cloud as a feature, a rule-based method involving principal component analysis, and models generated from a combination of these models. In addition, we focus on modeling overhead cables from field data (urban and suburban). Results show the practicability of modeling overhead cables with a cable length of 10-70 m regardless of the area type. We find that the best cable modeling rate with the precision and recall of 80.76% and 83.84%, respectively, can be obtained using the machine-learning method and by specifying the cable reproduction rate to be 2 m.
机译:我们可以通过使用点云通过3D结构可视化获得的3D结构信息的特性来自动化基础设施设施的检查工作。然后可以定量诊断设备的安全水平。在本文中,我们研究了电线结构的建模,例如靠近地面的公用事业杆之间的架空通信电缆,具有许多障碍物,具有复杂的结构。我们评估了电缆模型的准确性并将它们与正确的模型进行比较。我们使用三种建模方法:一种基于点云的挤出表面的机器学习方法作为特征,一种基于规则的方法,涉及主成分分析,以及由这些模型的组合产生的模型。此外,我们专注于从现场数据(城市和郊区)建模架空电缆。结果显示,无论面积类型如何,电缆长度为10-70米的电缆长度建模的实用性。我们发现,使用机器学习方法可以获得最佳电缆建模率80.76%和83.84%,并通过将电缆再现率指定为2米。

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