基于LiDAR点云的电力塔倾斜估计算法

Falin Chen, Yong Li, Shuang Feng, Mingmin Huang
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引用次数: 0

摘要

电塔是电网中输电的基础设施,为了保证输电的安全,需要对电塔的状态进行定期检查。为了提高检测效率,本文提出了一种基于无人机激光雷达点云功率检测方法的塔架倾斜检测方法。该方法在无人机采集电力走廊数据后,利用SCF-Net网络对场景中的电力目标进行语义分割,然后利用K-means聚类方法获得单个塔。然后利用提取的单个塔,提出了一种基于交叉拉伸点云平面拟合和法向估计的塔倾斜估计算法。利用实际采集的电力走廊数据进行实验,实验结果表明,SCF-Net网络对电线杆分类效果的IOU指数达到97.75,能够准确判断出未倾斜电线杆和人工放置倾斜电线杆。与其他方法相比,本文算法自动化程度高,不需要过多的人为干预,具有工程应用价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
PTTE:Power Tower Tilt Estimation Algorithm based on LiDAR Point Cloud
Power towers are the infrastructure for power transmission in the power grid, and to ensure the safety of power transmission, the state of power towers needs to be inspected regularly. To improve inspection efficiency, this paper proposes a method for detecting the tilt of towers based on the power inspection method of the UAV LiDAR point cloud. The method uses SCF-Net network to semantically segment the power targets in the scene after the UAV has collected the power corridor data, and then uses K-means clustering to obtain individual towers. Then the extracted individual pylons are used to propose a pylon tilt estimation algorithm based on cross-stretcher point cloud plane fitting and normal estimation. Experiments are conducted with the actual collected power corridor data, and the experimental results show that the IOU index of the classification effect of the SCF-Net network on power poles reaches 97.75, and the untilted poles and manually placed tilted poles can be accurately judged. Compared with other methods, the proposed algorithm has a high degree of automation, does not require too much human intervention, and has engineering application value.
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