Automatic Vectorization of Power Lines from Airborne Lidar Point Clouds

E. Maset, Andrea Fusiello
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Abstract

Abstract. In recent years, power line inspections have benefited from the use of the lidar surveying technology, which enables safe and rapid data acquisition, even in challenging environments. To further optimize monitoring operations and reduce time and costs, automatic processing of the point clouds obtained is of greatest importance. This work presents a complete pipeline for processing power line data that includes (i) lidar point cloud segmentation using a Fully Convolutional Network, (ii) individual pylon identification via DBSCAN clustering, and (iii) the automatic extraction and modelling of any number of cables using a multi-model fitting algorithm based on the J-Linkage method. The proposed procedure is tested on a 36 km-long power line, resulting in a F1-score of 97.6% for pylons and 98.5% for the vectorized cables.
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从机载激光雷达点云自动矢量化电力线
摘要近年来,激光雷达测量技术的使用使电力线路检测工作受益匪浅,即使在充满挑战的环境中也能安全快速地采集数据。为了进一步优化监测工作,减少时间和成本,对所获得的点云进行自动处理就显得尤为重要。本研究提出了一套完整的电力线数据处理流程,其中包括:(i) 使用全卷积网络进行激光雷达点云分割;(ii) 通过 DBSCAN 聚类进行单个塔架识别;(iii) 使用基于 J-Linkage 方法的多模型拟合算法自动提取任意数量的电缆并为其建模。建议的程序在 36 公里长的电力线上进行了测试,结果塔架的 F1 分数为 97.6%,矢量化电缆的 F1 分数为 98.5%。
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