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.

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 Improvement of 3D Power Line Extraction from Multiple Low-Cost UAV Imagery Using Wavelet Analysis

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 700 ◽  
Author(s):  
Anna Fryskowska
Keyword(s):  
Point Cloud ◽  
Accuracy Assessment ◽  
Low Cost ◽  
Three Dimensional ◽  
Point Clouds ◽  
Power Line ◽  
Power Lines ◽  
Remotely Sensed Data ◽  
Processing Algorithms

Three-dimensional (3D) mapping of power lines is very important for power line inspection. Many remotely-sensed data products like light detection and ranging (LiDAR) have been already studied for power line surveys. More and more data are being obtained via photogrammetric measurements. This increases the need for the implementation of advanced processing techniques. In recent years, there have been several developments in visualisation techniques using UAV (unmanned aerial vehicle) platform photography. The most modern of such imaging systems have the ability to generate dense point clouds. However, image-based point cloud accuracy is very often various (unstable) and dependent on the radiometric quality of images and the efficiency of image processing algorithms. The main factor influencing the point cloud quality is noise. Such problems usually arise with data obtained via low-cost UAV platforms. Therefore, generated point clouds representing power lines are usually incomplete and noisy. To obtain a complete and accurate 3D model of power lines and towers, it is necessary to develop improved data processing algorithms. The experiment tested the algorithms on power lines with different voltages. This paper presents the wavelet-based method of processing data acquired with a low-cost UAV camera. The proposed, original method involves the application of algorithms for coarse filtration and precise filtering. In addition, a new way of calculating the recommended flight height was proposed. At the end, the accuracy assessment of this two-stage filtration process was examined. For this, point quality indices were proposed. The experimental results show that the proposed algorithm improves the quality of low-cost point clouds. The proposed methods improve the accuracy of determining the parameters of the lines by more than twice. About 10% of noise is reduced by using the wavelet-based approach.

scholarly journals Fast and Accurate Power Line Corridor Survey Using Spatial Line Clustering of Point Cloud

2021 ◽  
Vol 13 (8) ◽  
pp. 1571
Author(s):  
Yuchun Huang ◽  
Yingli Du ◽  
Wenxuan Shi
Keyword(s):  
High Voltage ◽  
Ground Truth ◽  
Point Clouds ◽  
Power Line ◽  
Power Lines ◽  
Spatial Distance ◽  
Stable Operation ◽  
3D Point Clouds ◽  
Overhead Power Lines ◽  
Power Line Corridor

High-voltage and ultra-high-voltage overhead power lines are important to meet the electricity demand of our daily activities and productions. Due to the overgrowth of trees/vegetation within the corridor area, the distance between the power lines and its surroundings may break through the safety threshold, which could cause potential hazards such as discharge and fire. To ensure the safe and stable operation of the power lines, it is necessary to survey them regularly so that the potential hazards from the surroundings within the power line corridor could be investigated timely. This paper is motivated to quickly and accurately survey the power line corridor with the 3D point clouds. The main contributions of this paper include: (1) the spatial line clustering is proposed to accurately classify and complete the power line points, which can greatly overcome the sparsity and missing of LiDAR points within the complex power line corridor. (2) The contextual relationship between power lines and pylon is well investigated by the grid-based analysis, so that the suspension points of power lines on the pylon are well located. (3) The catenary plane-based simplification of 3D spatial distance calculation between power lines and ground objects facilitates the survey of the power line corridor. Experimental results show that the accuracy of safety distance surveying is 5 cm for power line corridors of all voltage levels. Compared to the ground-truth point-to-point calculation, the speed of surveying is enhanced thousands of times. It is promising to greatly improve both the accuracy and efficiency of surveying the potential hazards of power line corridor.

scholarly journals MINI-UAV LIDAR FOR POWER LINE INSPECTION

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 297-300 ◽  
Author(s):  
G. E. Teng ◽  
M. Zhou ◽  
C. R. Li ◽  
H. H. Wu ◽  
W. Li ◽  
...  
Keyword(s):  
Complex Terrain ◽  
Point Clouds ◽  
Power Line ◽  
Research Field ◽  
Inspection System ◽  
Lidar System ◽  
Cloud Data ◽  
System Solution ◽  
Aerial Vehicle ◽  
Academy Of Sciences

Light detection and ranging (LIDAR) system based on unmanned aerial vehicles (UAVs) recently are in rapid advancement, meanwhile portable and flexible mini-UAV-borne laser scanners have been a hot research field, especially for the complex terrain survey in the mountains and other areas. This study proposes a power line inspection system solution based on mini-UAV-borne LIDAR system&amp;ndash;AOEagle, developed by Academy of Opto-Electronics, Chinese Academy of Sciences, which mounted on a Multi-rotor unmanned aerial vehicle for complex terrain survey according to real test. Furthermore, the point cloud data was explored to validate its applicability for power line inspection, in terms of corridor and line laser point clouds; deformation detection of power towers, etc. The feasibility and advantages of AOEagle have been demonstrated by the promising results based on the real-measured data in the field of power line inspection.

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 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 DEVELOPMENT AND TEST OF BLIMP-BASED COMPACT LIDAR POWEWR-LINE INSPECTION SYSTEM

2015 ◽  
Vol XL-3/W2 ◽  
pp. 155-159
Author(s):  
W. W. Pan ◽  
Y. J. Dou ◽  
G. L. Wang ◽  
M. X. Wu ◽  
R. G. Ren ◽  
...  
Keyword(s):  
Transmission Line ◽  
Power Line ◽  
Power Lines ◽  
Laser Ranging ◽  
Inspection System ◽  
Lidar System ◽  
Overhead Transmission Line ◽  
Manual Inspection ◽  
High Efficient

This paper introduces a compact LIDAR system designed to inspect overhead transmission line for maintenance purposes. This LIDAR system is carried by a small unmanned helium airship, which is guided by GPS and laser ranging to fly automatically along the power-line over a limited distance. The 3D coordinates of the power line, power tower and power line channel features are gathered by LIDAR. Test have been accomplished using this blimp-based compact LIDAR power-line inspection system. Its inspections of a 500kV power lines also shows the high efficient inspection, less risk to personnel and more inspections per day compared with manual inspection.

scholarly journals AERIAL-TRIANGULATION AIDED BORESIGHT CALIBRATION FOR A LOW-COST UAV-LIDAR SYSTEM

2020 ◽  
Vol V-1-2020 ◽  
pp. 245-252
Author(s):  
J. Li ◽  
B. Yang ◽  
C. Chen ◽  
W. Wu ◽  
L. Zhang
Keyword(s):  
High Precision ◽  
Average Distance ◽  
Low Cost ◽  
Dynamic Network ◽  
Point Clouds ◽  
Systematic Errors ◽  
Lidar System ◽  
Aerial Triangulation ◽  
System States ◽  
The Impact

Abstract. The Laser-IMU boresight calibration is the precondition for an Unmanned Aerial Vehicle (UAV)-Light Detection and Ranging (LiDAR) system (ULS). The existing methods achieve good performance for calibrating ULSs with high-precision positioning and orientation systems (POS) (e.g., APX-15), in which, the systematic errors of the high-precision POS can be ignored, only the boresight parameters are estimated. However, these methods have difficulties in calibrating the low-cost ULSs with low-precision POS. To overcome the impact of the systematic errors of the low-precision POS on boresight calibration, an aerial-triangulation aided boresight calibration is proposed in this paper. It simultaneously estimates the laser-IMU boresight angles and system states (e.g. trajectory) by setting the point clouds derived from aerial-triangulation (AT point clouds) as the reference. Firstly, the planar voxels from the AT point clouds are extracted, due to the fact that they are more reliable in AT point clouds. Secondly, raw laser observations are matched with the extracted planar voxels to establish laser matching observations. Thirdly, a Dynamic Network (DN) is built using the GNSS observations, inertial observations, and laser matching observations to simultaneously optimize the initial laser-IMU boresight angles and the system states. All the sensor observations involved in the ULS are modeled with proper error models, which are essential for analyzing and refining the data quality of the low-cost ULS. The proposed method was tested to calibrate a low-cost ULS, KylinCloud-II, in a calibration field. It showed that the average distance between the laser point clouds and the referenced AT point clouds was decreased from 2.560m (RMSE = 3.88m) to 0.08m (RMSE = 0.99m).

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 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.

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