For periodic monitoring of power utilities, there has been keen interest by utility companies to extract the powerlines from laser scanning data. However, challenges arise when utilizing large point clouds as well as avoiding false positives or other errors in the extraction due to noise from objects in close proximity to the powerlines. In this study, we propose an efficient and robust approach to overcome these challenges through two main steps: candidate powerline point extraction and refinement. In the candidate powerline point extraction step, a voxel-based subsampling structure temporarily substitutes the original scan points with regularly spaced subsampled points that still preserve key details present within the point cloud but significantly reduce the dataset size. After removing the ground surface and adjacent objects, candidate powerline points are efficiently extracted through a hierarchical, feature-based filtering process. In the refinement step, the link between the subsampled candidate powerline points and original scan point cloud enable the original points to be segmented and grouped into clusters. By fitting mathematical models, an individual powerline is re-clustered and used to reconstruct the broken sections in the powerlines. The proposed approach is evaluated on 30 unique datasets with different powerline configurations acquired at five different sites by either a terrestrial or mobile laser scanning system. The parameters are optimized through a sensitivity analysis with pointwise comparison between the extracted powerlines and ground truth using 10 diverse datasets, demonstrating that only one requisite parameter varied as a function of resolution while the remaining parameters were generally consistent across the datasets. With optimized parameters, the proposed approach achieved F1 scores of 88.87–95.47% with high efficiency ranging from 0.81 and 1.46 million points/sec when tested on 30 datasets.

对于电力公用事业的定期监视，公用事业公司强烈希望从激光扫描数据中提取电力线。然而，当利用大的点云以及避免由于来自电力线附近的物体的噪声而导致的误报或其他提取错误时，就会出现挑战。在这项研究中，我们提出了一种有效而强大的方法来通过两个主要步骤来克服这些挑战：候选电力线点提取和优化。在候选电力线点提取步骤中，基于体素的子采样结构将原始扫描点临时替换为规则间隔的子采样点，这些子采样点仍保留点云中存在的关键细节，但会大大减小数据集的大小。去除地面和邻近物体后，通过基于特征的分层过滤过程，可以有效地提取候选电力线点。在优化步骤中，子采样的候选电力线点和原始扫描点云之间的链接使原始点可以被分割并分组为群集。通过拟合数学模型，可以对单个电力线进行聚类，并用于重建电力线中的断面。通过地面或移动激光扫描系统在30个具有不同电力线配置的独特数据集上评估了所提出的方法，这些数据集在五个不同地点获得。通过灵敏度分析对参数进行优化，并使用10个不同的数据集对提取的电力线和地面真实情况进行逐点比较，证明只有一个必要参数随分辨率而变化，而其余参数在整个数据集中通常是一致的。通过优化参数，该方法在30个数据集上进行测试时，其F1分数达到88.87–95.47％，效率介于0.81和146万点/秒之间。