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3组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值分别可达91.6％、96.0％、93.5％,杆塔定位精度保持在分米级甚至厘米级.
Automatic extraction of high-voltage transmission pylons with multifeature constraints
Pylons are 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 a balanced and tremendous amount of training samples or lack sufficient terrain adaptability.Furthermore,these methods are vulnerable to tall objects,such as trees and buildings in the complex terrain environment of the mountainous areas.This study proposes an automatic pylon extraction method based on multifeature constraints.First,the height above the ground and the maximum vertical gap are designed on the basis of the spatial distribution of objects in the transmission corridor point clouds.Second,a series of preprocessing tasks,such as denoising and filtering,is performed on airborne LiDAR point clouds.Third,the pylon regions are quickly located on the basis of multifeature 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 nonpylon point clouds are eliminated layer by layer using the gap between the interference and the pylon vertical slicing.Airborne LiDAR point clouds in three different scenarios are utilized to evaluate the performance of the proposed method.The root mean square error of the pylon center coordinates are 0.04,0.40,and 0.13 m.The precision,recall,and Fl-value of the pylon extraction can reach up to 91.6％,96.0％,and 93.5％.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.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 consider class-imbalance problems.Furthermore,the proposed method can provide auxiliary information for postprocessing,such as scene classification and line hanging point extraction,and promote the application of airborne LiDAR for power inspection.
airborne LiDARmultifeature constrainttransmission corridorvertical slicingautomatic extraction of pylons
万方数据
维普
