多特征约束的输电通道杆塔点云提取研究

王濮; 王成; 习晓环; 聂胜; 杜蒙

doi:10.11834/jrs.20232684

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多特征约束的输电通道杆塔点云提取研究
Automatic Extraction of High-Voltage Transmission Pylons with Multi Feature Constraints
2023年页码：1-12
DOI： 10.11834/jrs.20232684

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王濮, 王成, 习晓环, 等. 多特征约束的输电通道杆塔点云提取研究[J/OL]. 遥感学报, 2023,1-12.

WANG Pu, WANG Cheng, XI Xiaohuan, et al. Automatic Extraction of High-Voltage Transmission Pylons with Multi Feature Constraints[J/OL]. National Remote Sensing Bulletin, 2023,1-12.
王濮, 王成, 习晓环, 等. 多特征约束的输电通道杆塔点云提取研究[J/OL]. 遥感学报, 2023,1-12. DOI： 10.11834/jrs.20232684.

WANG Pu, WANG Cheng, XI Xiaohuan, et al. Automatic Extraction of High-Voltage Transmission Pylons with Multi Feature Constraints[J/OL]. National Remote Sensing Bulletin, 2023,1-12. DOI： 10.11834/jrs.20232684.

摘要

杆塔自动识别是机载LiDAR（Light Detection and Ranging）电力巡检应用的重要内容，特别是长距离、规模化应用时，高效高精度的杆塔点云提取尤为重要。针对复杂地形环境下输电通道杆塔点云快速精准识别难的问题，本文提出了一种基于多特征约束的杆塔点云自动提取方法。首先，基于输电通道地物空间分布特点，设计了离地高度、垂直最大间隙等特征；其次，对输电通道机载LiDAR点云进行去噪、滤波等一系列预处理；然后，对非地面点云进行网格化，基于离地高差、线性度等多特征约束快速定位杆塔区域，并利用分层密度法和杆塔塔体结构对称性提取杆塔中心坐标；最后，对杆塔区域点云垂直分层切片，逐层剔除非杆塔点云。采用三种不同场景的机载点云数据进行算法验证，结果表明本文所提方法可从原始点云中快速自动提取杆塔点，其中查准率、召回率、F1值可达0.916、0.960、0.935，杆塔定位精度保持在分米级甚至厘米级。

Abstract

Objective The pylon is an important component of the transmission line and its identification based on airborne light detection and ranging (LiDAR) is crucial to power inspection. The efficient and high-precision extraction of pylon point clouds is important, especially in long-distance and large-scale applications, and is also conducive to massive data organization, parallel processing, and quantitative applications. The existing pylon extraction methods usually require balanced and tremendous amount of training samples or lack sufficient terrain adaptability. Furthermore, these methods are vulnerable to the high height objects such as trees and buildings in the complex terrain environment of the mountainous areas. Method This study proposed a method of automatic pylon extraction based on the multi-feature constraints. First, the height above the ground and the maximum vertical gap are designed based on the spatial distribution of objects in the transmission corridor point clouds. Second, a series of preprocessing such as denoising and filtering are performed on airborne LiDAR point clouds. Third, the pylon regions are quickly located based on multi-feature constraints such as height difference and linearity, and the pylon center coordinates are calculated by using the layered density method and pylon structural symmetry. Finally, the point clouds of pylon regions are vertically sliced along the Z axis, and the non-pylon point clouds are eliminated layer by layer using the gap between the interference and the pylon vertical slicing. Result Airborne LiDAR point clouds in three different scenarios are utilized to evaluating the performance of the proposed method. The root mean square error (RMSE) of the pylon center coordinates are 0.04m, 0.40m and 0.13m, respectively. The precision, recall and F1-value of the pylon extraction can reach up to 0.916, 0.960 and 0.935, respectively. Compared with other pylon extraction methods, the qualitative analysis results show that the proposed method performs better in pylon area recognition, positioning error and pylon point cloud extraction. Meanwhile, the proposed method successfully extracts pylons from variable terrain point clouds. Conclusion The aforementioned experimental results show that the proposed method can effectively extract pylons with high accuracy and strong terrain adaptability. In addition, the method does not need to train samples and take into account class-imbalance problems. And the proposed method can provide auxiliary information for post-processing such as scene classification, line hanging point extraction, etc., and promote the application of airborne LiDAR for power inspection.

关键词

机载LiDAR点云多特征约束输电通道垂直分层切片杆塔自动提取

Keywords

Airborne LiDARMulti-feature constraintTransmission corridorVertical slicingAutomatic extraction of pylons

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