scholarly journals AUTOMATIC RAILWAY POWER LINE EXTRACTION USING MOBILE LASER SCANNING DATA

2016 ◽  
Vol XLI-B5 ◽  
pp. 615-619 ◽  
Author(s):  
Shanxin Zhang ◽  
Cheng Wang ◽  
Zhuang Yang ◽  
Yiping Chen ◽  
Jonathan Li
Keyword(s):  
Laser Scanning ◽  
Region Growing ◽  
Point Clouds ◽  
Power Line ◽  
Power Lines ◽  
Practical Significance ◽  
Space Region ◽  
Extraction Technology ◽  
Line Extraction ◽  
Fitting Algorithm

Research on power line extraction technology using mobile laser point clouds has important practical significance on railway power lines patrol work. In this paper, we presents a new method for automatic extracting railway power line from MLS (Mobile Laser Scanning) data. Firstly, according to the spatial structure characteristics of power-line and trajectory, the significant data is segmented piecewise. Then, use the self-adaptive space region growing method to extract power lines parallel with rails. Finally use PCA (Principal Components Analysis) combine with information entropy theory method to judge a section of the power line whether is junction or not and which type of junction it belongs to. The least squares fitting algorithm is introduced to model the power line. An evaluation of the proposed method over a complicated railway point clouds acquired by a RIEGL VMX450 MLS system shows that the proposed method is promising.

scholarly journals AUTOMATIC RAILWAY POWER LINE EXTRACTION USING MOBILE LASER SCANNING DATA

2016 ◽  
Vol XLI-B5 ◽  
pp. 615-619
Author(s):  
Shanxin Zhang ◽  
Cheng Wang ◽  
Zhuang Yang ◽  
Yiping Chen ◽  
Jonathan Li
Keyword(s):  
Laser Scanning ◽  
Region Growing ◽  
Point Clouds ◽  
Power Line ◽  
Power Lines ◽  
Practical Significance ◽  
Space Region ◽  
Extraction Technology ◽  
Line Extraction ◽  
Fitting Algorithm

Research on power line extraction technology using mobile laser point clouds has important practical significance on railway power lines patrol work. In this paper, we presents a new method for automatic extracting railway power line from MLS (Mobile Laser Scanning) data. Firstly, according to the spatial structure characteristics of power-line and trajectory, the significant data is segmented piecewise. Then, use the self-adaptive space region growing method to extract power lines parallel with rails. Finally use PCA (Principal Components Analysis) combine with information entropy theory method to judge a section of the power line whether is junction or not and which type of junction it belongs to. The least squares fitting algorithm is introduced to model the power line. An evaluation of the proposed method over a complicated railway point clouds acquired by a RIEGL VMX450 MLS system shows that the proposed method is promising.

scholarly journals AUTOMATIC EXTRACTION OF POWER LINES FROM UAV LIDAR POINT CLOUDS USING A NOVEL SPATIAL FEATURE

2019 ◽  
Vol IV-2/W7 ◽  
pp. 227-234
Author(s):  
M. Zhou ◽  
K. Y. Li ◽  
J. H. Wang ◽  
C. R. Li ◽  
G. E. Teng ◽  
...  
Keyword(s):  
Point Cloud ◽  
Real Data ◽  
Point Clouds ◽  
Power Line ◽  
Power Lines ◽  
Point Cloud Data ◽  
Point Sets ◽  
Linear Discriminant ◽  
Cloud Data ◽  
Line Extraction

<p><strong>Abstract.</strong> UAV LiDAR systems have unique advantage in acquiring 3D geo-information of the targets and the expenses are very reasonable; therefore, they are capable of security inspection of high-voltage power lines. There are already several methods for power line extraction from LiDAR point cloud data. However, the existing methods either introduce classification errors during point cloud filtering, or occasionally unable to detect multiple power lines in vertical arrangement. This paper proposes and implements an automatic power line extraction method based on 3D spatial features. Different from the existing power line extraction methods, the proposed method processes the LiDAR point cloud data vertically, therefore, the possible location of the power line in point cloud data can be predicted without filtering. Next, segmentation is conducted on candidates of power line using 3D region growing method. Then, linear point sets are extracted by linear discriminant method in this paper. Finally, power lines are extracted from the candidate linear point sets based on extension and direction features. The effectiveness and feasibility of the proposed method were verified by real data of UAV LiDAR point cloud data in Sichuan, China. The average correct extraction rate of power line points is 98.18%.</p>

scholarly journals Classification of high-voltage power line structures in low density ALS data acquired over broad non-urban areas

2021 ◽  
Vol 7 ◽  
pp. e672
Author(s):  
Jean-Romain Roussel ◽  
Alexis Achim ◽  
David Auty
Keyword(s):  
High Voltage ◽  
Laser Scanning ◽  
Point Clouds ◽  
Power Line ◽  
Power Lines ◽  
Electrical Network ◽  
Low Density ◽  
Area Of Interest ◽  
Transmission Towers ◽  
High Voltage Power Lines

Airborne laser scanning (ALS) has gained importance over recent decades for multiple uses related to the cartography of landscapes. Processing ALS data over large areas for forest resource estimation and ecological assessments requires efficient algorithms to filter out some points from the raw data and remove human-made structures that would otherwise be mistaken for natural objects. In this paper, we describe an algorithm developed for the segmentation and cleaning of electrical network facilities in low density (2.5 to 13 points/m2) ALS point clouds. The algorithm was designed to identify transmission towers, conductor wires and earth wires from high-voltage power lines in natural landscapes. The method is based on two priors i.e. (1) the availability of a map of the high-voltage power lines across the area of interest and (2) knowledge of the type of transmission towers that hold the conductors along a given power line. It was tested on a network totalling 200 km of wires supported by 415 transmission towers with diverse topographies and topologies with an accuracy of 98.6%. This work will help further the automated detection capacity of power line structures, which had previously been limited to high density point clouds in small, urbanised areas. The method is open-source and available online.

scholarly journals Segmentation and Multi-Scale Convolutional Neural Network-Based Classification of Airborne Laser Scanner Data

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3347 ◽  
Author(s):  
Zhishuang Yang ◽  
Bo Tan ◽  
Huikun Pei ◽  
Wanshou Jiang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Region Growing ◽  
Point Clouds ◽  
Classification Method ◽  
Multi Scale ◽  
Airborne Laser

The classification of point clouds is a basic task in airborne laser scanning (ALS) point cloud processing. It is quite a challenge when facing complex observed scenes and irregular point distributions. In order to reduce the computational burden of the point-based classification method and improve the classification accuracy, we present a segmentation and multi-scale convolutional neural network-based classification method. Firstly, a three-step region-growing segmentation method was proposed to reduce both under-segmentation and over-segmentation. Then, a feature image generation method was used to transform the 3D neighborhood features of a point into a 2D image. Finally, feature images were treated as the input of a multi-scale convolutional neural network for training and testing tasks. In order to obtain performance comparisons with existing approaches, we evaluated our framework using the International Society for Photogrammetry and Remote Sensing Working Groups II/4 (ISPRS WG II/4) 3D labeling benchmark tests. The experiment result, which achieved 84.9% overall accuracy and 69.2% of average F1 scores, has a satisfactory performance over all participating approaches analyzed.

scholarly journals An Efficient and Accurate Method for Different Configurations Railway Extraction Based on Mobile Laser Scanning

2019 ◽  
Vol 11 (24) ◽  
pp. 2929 ◽  
Author(s):  
Rong Zou ◽  
Xiaoyun Fan ◽  
Chuang Qian ◽  
Wenfang Ye ◽  
Peng Zhao ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Point Clouds ◽  
Accurate Method ◽  
Average Density ◽  
Railway Track ◽  
Track Geometry ◽  
Fitting Algorithm ◽  
Railway Maintenance ◽  
Night Period ◽  
Reflection Intensity

The precision of railway map is becoming a significant issue for autonomous train scheduling, monitoring and maintenance, related location-based service (LBS), and further ensuring travel safety. Mobile 3D laser scanning is an efficient method for making relative high-precision railway track maps, particularly during the night period of railway maintenance, for light detection and ranging (LiDAR) can work without ambient light. In this paper, we propose an efficient and accurate railway track vectorization method based on the LiDAR point clouds from the self-built train Mobile Laser Scanning (MLS) system. Our method takes full use of railway track geometry and reflection intensity feature of LiDAR, without any trajectory prior information. Firstly, clear track points are filtered by intensity; then, a K-means clustering fused Region-Grow Fitting algorithm is applied. It can not only extract the line vector of railway track, but also can tell the track branches apart, especially on bends and turnout. Experiments were carried on using point clouds with an average density of 490 points per square meter. The experimental results show that the method not only can quickly extract linear objects such as railway track and catenary, but also can detect the railways even in complex real-world topologies such as at bends and turnouts. The precision of the detection area in bends and turnouts are 90.32% and 81.31% respectively, the sensitivity is 83.27% and 83.33%, respectively. Moreover, it can identify the track networks.

scholarly journals AUTOMATIC DETECTION AND CHARACTERISATION OF POWER LINES AND THEIR SURROUNDINGS USING LIDAR DATA

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1161-1168 ◽  
Author(s):  
M. Yermo ◽  
J. Martínez ◽  
O. G. Lorenzo ◽  
D. L. Vilariño ◽  
J. C. Cabaleiro ◽  
...  
Keyword(s):  
Low Voltage ◽  
Parallel Implementation ◽  
Point Clouds ◽  
Power Line ◽  
Power Lines ◽  
Computational Time ◽  
Efficient Memory ◽  
3D Lidar ◽  
Very High ◽  
Line Classification

<p><strong>Abstract.</strong> Light Detection and Ranging (LiDAR) is nowadays one of the most used tools to obtain geospatial data. In this paper, a method to detect and characterise power lines of both high and low voltage and their surroundings from 3D LiDAR point clouds exclusively is proposed. First, to identify points of the power lines a global search of candidate points is carried out based on the height of each point compared to its neighbours. Then, the Hough Transform (HT) is applied on the set of candidate points to extract the catenaries that belong to each power line, allowing the identification of each conductor individually. Finally, conductors located on the same power line are grouped, their geometric characteristics analysed, and the quantitative features of the surroundings are computed. A very high accuracy of power line classification is reached with these methods, while the computational time is optimised by efficient memory usage and parallel implementation of the code.</p>

scholarly journals An Entropy-Weighting Method for Efficient Power-Line Feature Evaluation and Extraction from LiDAR Point Clouds

2021 ◽  
Vol 13 (17) ◽  
pp. 3446
Author(s):  
Junxiang Tan ◽  
Haojie Zhao ◽  
Ronghao Yang ◽  
Hua Liu ◽  
Shaoda Li ◽  
...  
Keyword(s):  
High Precision ◽  
Ground Surface ◽  
Point Clouds ◽  
Power Line ◽  
Power Lines ◽  
Lidar System ◽  
Weighting Method ◽  
Feature Evaluation ◽  
Vertical Range ◽  
3D Information

Power-line inspection is an important means to maintain the safety of power networks. Light detection and ranging (LiDAR) technology can provide high-precision 3D information about power corridors for automated power-line inspection, so there are more and more utility companies relying on LiDAR systems instead of traditional manual operation. However, it is still a challenge to automatically detect power lines with high precision. To achieve efficient and accurate power-line extraction, this paper proposes an algorithm using entropy-weighting feature evaluation (EWFE), which is different from the existing hierarchical-multiple-rule evaluation of many geometric features. Six significant features are selected (Height above Ground Surface (HGS), Vertical Range Ratio (VRR), Horizontal Angle (HA), Surface Variation (SV), Linearity (LI) and Curvature Change (CC)), and then the features are combined to construct a vector for quantitative evaluation. The feature weights are determined by an entropy-weighting method (EWM) to achieve optimal distribution. The point clouds are filtered out by the HGS feature, which possesses the highest entropy value, and a portion of non-power-line points can be removed without loss of power-line points. The power lines are extracted by evaluation of the other five features. To decrease the interference from pylon points, this paper analyzes performance in different pylon situations and performs an adaptive weight transformation. We evaluate the EWFE method using four datasets with different transmission voltage scales captured by a light unmanned aerial vehicle (UAV) LiDAR system and a mobile LiDAR system. Experimental results show that our method demonstrates efficient performance, while algorithm parameters remain consistent for the four datasets. The precision F value ranges from 98.4% to 99.7%, and the efficiency ranges from 0.9 million points/s to 5.2 million points/s.

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