Power Line Extraction and Tree Risk Detection Based on Airborne LiDAR
Abstract
:1. Introduction
- (1)
- Although the power line extraction methods based on linear features are mainstream, they rely heavily on geometric constraints and preset parameters, ignore the spatial distribution features of transmission lines, and do not further distinguish differences between aerial ground wires and conductors, which is not conducive to the efficient inspection of transmission lines.
- (2)
- Automated and intelligent tree risk detection methods based on 3D point clouds focus on how to use different heights of spatial segmentation methods, different neighborhood space determination methods, and different point cloud index structures to achieve the rapid point-to-point or point-to-line safety distance calculation. However, they ignore the obvious spatial distribution features of transmission lines and the actual situation, in which the number of tree risk points is small, resulting in a large number of non-essential safety distance calculations and judgments.
- (1)
- The local dimensional feature probability model of a point cloud under the restriction of minimum information entropy is proposed to realize the accurate extraction of power lines, and the method still has good applicability for complex scenarios.
- (2)
- The cloth simulation filtering (CSF) algorithm [48] and region growth method based on the neighborhood sharing degree are used to achieve an accurate distinction between ground wires and conductors.
- (3)
- The candidate area of tree risk points centered on the conductor reconstruction curve based on a catenary-linear equation is constructed, and a safety distance grading calculation strategy is proposed to realize the accurate detection of tree risk points.
2. Methods
2.1. Overall Architecture
2.2. Spatial Distribution Feature Analysis of Transmission Line
- (1)
- As shown in Figure 2a, power lines are suspended from the tower in the form of a catenary and the ground wires are above the conductors. The distances between different power lines are kept fixed and distributed approximately in parallel. A single power line is closely connected end to end in the horizontal direction with obvious linear features. However, the vertical distribution of the power line is extremely discontinuous.
- (2)
- Within the same size area, there are significantly more vegetation points than power line points, which means that when counting some of the indicators related to the point coordinates or numbers, the vegetation points are given more weight. Randomized comparisons of power line point density and vegetation point density are both applicable, as shown in Figure 2b.
2.3. Power Line Extraction
2.3.1. Coarse Extraction of Power Lines Based on Height Difference
- (1)
- In order to ensure the integrity of the transmission line, the original point cloud width is much larger than the transmission line corridor width. Therefore, the point cloud needs to be clipped according to the coordinates of the pylons already known by the State Grid Corporation of China and the specified corridor width, which is generally 100 m. The original point cloud is divided into multiple point clouds that are end to end, each containing two adjacent towers, power lines, and other features, as shown in Figure 3.
- (2)
- In order to eliminate the influence of terrain undulation, facilitate subsequent point elevation statistics, and distinguish ground and non-ground points, an improved progressive TIN densification filtering algorithm is used to obtain the terrain of the transmission line corridor [49]; then, the point cloud is elevation normalized based on the ground point, as shown in Figure 4.
- (3)
- Starting from the ground, the number of non-ground points corresponding to different elevation ladders is counted with 1 m as the step length. The reason for the 1 m step length is that there is more low vegetation and less medium and high vegetation, and vegetation that is 0–1 m above ground level is considered low vegetation. As shown in Figure 5, the elevation of the points is mainly within 10 m; this predominantly comprises low vegetation close to the ground, with the most points within 0–1 m. However, there are very few points with an elevation greater than 10 m; these are mainly power line points and pylon points.
- (4)
- The Z standard deviation [50] of the non-ground points is calculated using Equation (1).
- (5)
- Points with elevations greater than the Z standard deviation are classified as power line candidate points, and converse situations are classified as vegetation points. The effect of power line coarse extraction is shown in Figure 6; all power line candidate points have been extracted completely and accurately, while a very small number of vegetation canopy points have also been categorized as power lines, such as the points in the blue circle in Figure 6a. This misclassification will be improved in the subsequent power line refined extraction.
- (6)
- Elevation-normalized point clouds are denormalized based on ground points to restore the original elevation of points, as shown in Figure 6b.
2.3.2. Refined Extraction of Power Lines Based on Local Dimensional Features Probability Model
- When , the local feature of the point cloud is one-dimensional linear;
- When , the local feature of the point cloud is two-dimensional planar;
- When , the local feature of the point cloud is three-dimensional spherical.
2.4. Distinction between Aerial Ground Wires and Conductors
2.4.1. Coarse Extraction of Aerial Ground Wires Based on CSF
2.4.2. Refined Clustering of Ground Wires Based on Degree of Neighborhood Sharing
2.5. 3D Reconstruction of Conductors
2.5.1. Linear Equation
2.5.2. Catenary Equation
2.6. Tree Risk Detection
2.6.1. Construction of Tree Risk Candidate Area
2.6.2. Rough Calculation of Safety Distance Based on Grid
2.6.3. Accurate Calculation of Point-to-Point Safety Distance
3. Experiments and Discussion
3.1. Datasets
3.2. Evaluation Methods
3.2.1. Evaluation of the Classification Accuracy of Ground Wires and Conductor Points
3.2.2. Evaluation of the Spatial Position Accuracy of Conductor 3D Reconstruction
3.2.3. Evaluation Accuracy of Tree Risk Detection
3.3. Experimental Results
3.3.1. The Classification Effect and Accuracy of Ground Wires and Conductor Points
3.3.2. Spatial Position Accuracy of Conductor 3D Reconstruction
3.3.3. The Accuracy of Tree Risk Detection
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Radius | Information Entropy | Probability of Being One-Dimensional | Probability of Being Two-Dimensional | Probability of Being Three-Dimensional |
---|---|---|---|---|
0.1 m | 1.108 | 0.157 | 0.441 | 0.402 |
0.2 m | 0.803 | 0.673 | 0.256 | 0.071 |
0.3 m | 0.357 | 0.823 | 0.119 | 0.058 |
0.4 m | 0.163 | 0.966 | 0.029 | 0.005 |
0.5 m | 0.565 | 0.177 | 0.021 | 0.802 |
0.6 m | 0.537 | 0.149 | 0.026 | 0.825 |
0.7 m | 0.507 | 0.025 | 0.134 | 0.841 |
Classification | Count | SD | Maximum | Minimum |
---|---|---|---|---|
Power line | 5000 | 0.235° | 4.393° | 0° |
Pylon | 5000 | 13.061° | 32.415° | 12.337° |
Vegetation | 5000 | 18.172° | 43.994° | 14.042° |
Voltage Level | Terrain | Distance | Number of Conductors |
---|---|---|---|
110 kV | flat | 376 m | 6 |
220 kV | hill | 227 m | 6 |
500 kV | mountain | 591 m | 3 |
110 kV | 220 kV | 500 kV | |
---|---|---|---|
TP | 63,587 | 89,345 | 159,859 |
FN | 638 | 785 | 1076 |
FP | 1009 | 1780 | 2347 |
Precision | 0.9844 | 0.9805 | 0.9855 |
Recall | 0.9900 | 0.9912 | 0.9933 |
F-score | 0.9880 | 0.9858 | 0.9894 |
110 kV | 220 kV | 500 kV | |
---|---|---|---|
TP | 18,521 | 15,793 | 21,776 |
FN | 138 | 137 | 142 |
FP | 309 | 326 | 334 |
Precision | 0.9836 | 0.9798 | 0.9849 |
Recall | 0.9926 | 0.9914 | 0.9935 |
F-score | 0.9881 | 0.9856 | 0.9892 |
Voltage Level | RMSE/cm | Emax/cm | Emin/cm |
---|---|---|---|
110 kV | 2.76 | 5.34 | 1.91 |
220 kV | 3.35 | 6.52 | 1.74 |
500 kV | 3.67 | 7.13 | 2.64 |
Voltage Level | Consistency of Quantity | Quantities of RVs | Quantities of Tree Risk Points Detected Using Proposed Method | MAE/cm | Efficiency (Point to Point) | Efficiency (Proposed Method) |
---|---|---|---|---|---|---|
110 kV | ✓ | 7 | 7 | 6.47 | 1.769 s | 0.663 s |
220 kV | 0 | 0 | 0 | 0 | 1.827 s | 0.729 s |
500 kV | ✓ | 3 | 3 | 5.53 | 2.003 s | 0.804 s |
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Xi, S.; Zhang, Z.; Niu, Y.; Li, H.; Zhang, Q. Power Line Extraction and Tree Risk Detection Based on Airborne LiDAR. Sensors 2023, 23, 8233. https://doi.org/10.3390/s23198233
Xi S, Zhang Z, Niu Y, Li H, Zhang Q. Power Line Extraction and Tree Risk Detection Based on Airborne LiDAR. Sensors. 2023; 23(19):8233. https://doi.org/10.3390/s23198233
Chicago/Turabian StyleXi, Siyuan, Zhaojiang Zhang, Yufen Niu, Huirong Li, and Qiang Zhang. 2023. "Power Line Extraction and Tree Risk Detection Based on Airborne LiDAR" Sensors 23, no. 19: 8233. https://doi.org/10.3390/s23198233
APA StyleXi, S., Zhang, Z., Niu, Y., Li, H., & Zhang, Q. (2023). Power Line Extraction and Tree Risk Detection Based on Airborne LiDAR. Sensors, 23(19), 8233. https://doi.org/10.3390/s23198233