A Novel Monocular Vision Technique for the Detection of Electric Transmission Tower Tilting Trend
Abstract
:1. Introduction
- Manual measurement requires on-site measurement with instruments, which is cumbersome and requires a lot of workforce and material resources. It is also complex and poses specific security risks due to the geographical conditions of transmission lines. UAV monitoring uses airborne laser radar to build three-dimensional point clouds, which are then projected onto two-dimensional planes and analyzed using correlation algorithms [15]. However, this method has a high cost and does not provide real-time monitoring. Video monitoring has a strong real-time capability and can offer the intuitive perception of external force intrusion and other field conditions. It can timely detect abnormal conditions of transmission channels, such as external force damage. However, it requires manual monitoring and manual analysis for multi-directional distance monitoring of transmission channels. Further, it cannot accurately obtain real-time data and timely collection of spatial status data of conductors [16].
- For distance monitoring of tree barriers, commonly used methods include ranging with an airborne laser scanning device, setting up radar or laser ranging device, processing aerial inspection video, building a tree line model to predict distance, and binocular vision image ranging [17]. These methods improve the efficiency of distance measurement of tree barriers but also have some problems [18]. For example, 3D modeling requires regular updating and reconstruction, and the workload is significant. The tree growth cycle model construction is complex, while the stereo matching of binocular vision ranging is complicated [19]. Moreover, these tree barrier ranging methods have high costs and cannot provide real-time dynamic data of transmission lines.
- The traditional multi-directional distance monitoring method for a transmission channel cannot provide real-time analysis and processing, abnormal alarm, and trend warning [20].
2. Contour Extraction of Transmission Tower
2.1. Edge Detection of the Modified Canny Algorithm
2.2. The RGB Converted to HSV Color Space
2.3. Tower Contour Extraction
2.4. Construction and algorithm
3. Establishment of Tower Tilt Calculation Model
3.1. Model Building
3.1.1. Camera Imaging Model
3.1.2. Pole-Tower Imaging Model
3.1.3. Tower Overlooking Imaging Model
3.2. Establishment and Analysis Model of the Distance from the Center of the Tower
- (1)
- According to the standard “Operation Regulations of Overhead territorial Lines (DS/CLC/TR 50412-1)”, set the tilt range of the pole and tower, and divide the dangerous image area;
- (2)
- Convert the pole and tower images to grayscale and binary image transformation;
- (3)
- Define the upper left corner of the binary image as the coordinate origin (0, 0), go through the fixed interval from left to right and from top to bottom, determine the pixel coordinates of the tower, and judge the top and bottom positions of the tower according to the y-axis pixel coordinates:
- (4)
- Perverse the outline of the tower, take any point on it as the high point and the bottom of the tower as the lowest point, and calculate the tower outline’s pixel height at any point from the bottom;
- (5)
- Calculate the actual height of the outline of the tower, the distance between the camera and the tower, and the distance from the known horizontal parts;
- (6)
- Compare the desired distance with the set distance threshold, determine the danger point, and mark it.
4. Experimental Results
4.1. Subjects and Apparatus
- (1)
- Obtain the first picture and use it as the reference image to find the vertical distance between the camera and the tower (P1) and the horizontal distance between the camera and the tower components (P2) using a laser rangefinder.
- (2)
- Process the captured image, calibrate the pixels, and find the pixel distance. The processed pictures are shown in Figure 11. After traversing, the coordinates obtained for the top of the tower and the base point are (277, 309) and (240, 590), respectively. Thus, the pixel height of the tower can be found according to the formula.
- (3)
- Obtain the camera installation and tower heights and find the vertical distance between the camera and the pole and tower (PZ) using the distance combined with the pole and tower imaging model obtained in Step 2.
- (4)
- Find the distance between the camera and the horizontal component (PQ) using OI;
- (5)
- Compare the desired distance PZ and PQ with P1 and P2 and find the error rate.
4.2. Results
4.3. Field Test
- (1)
- Collect the parameters such as gear pitch, tower height, and tower width;
- (2)
- Measure the field data through the rangefinder;
- (3)
- Calculate the distance between the tower and the camera and the distance between its own horizontal components according to the tower tilt model and algorithm, combined with the parameters of the tower;
- (4)
- Compare the calculated data with the measured data, and correct the distance based on the comparison results.
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Order Number | The Camera Is Far away from the Pole and Tower Pz (m) | Range Error (%) | Axial Eccentric Distance (cm) | Range Error (%) |
---|---|---|---|---|
1 | 14.8 | 1.33 | 1.6 | 2 |
2 | 14.6 | 2.67 | 1.7 | 1.5 |
3 | 14.8 | 1.33 | 1.8 | 1 |
4 | 14.5 | 3.33 | 1.7 | 1.5 |
5 | 14.6 | 2.67 | 1.5 | 2.5 |
6 | 14.7 | 2 | 1.6 | 2 |
7 | 14.6 | 2.67 | 1.8 | 1 |
8 | 14.5 | 3.34 | 1.7 | 1.5 |
9 | 14.6 | 2.67 | 1.7 | 1.5 |
10 | 14.7 | 2 | 1.8 | 1 |
11 | 14.8 | 1.33 | 1.5 | 2.5 |
Measuring Position | Measured Value (m) | Actual Value (m) | Error (%) |
---|---|---|---|
The camera is far away from the pole and tower (Pz) | 189 | 190 | 0.5% |
Distance of the horizontal parts of the tower (PQ) | 22.5 | 23.0 | 2.2% |
Pole tower height (Py) | 234.0 | 235.0 | 0.4% |
Actual Tilt Angle/° | Angular Mean Calculated from Acceleration/° | A = 0.25 | Relative Accuracy/% | A = 0.5 | Relative Accuracy/% | A = 0.75 | Relative Accuracy/% |
---|---|---|---|---|---|---|---|
0 | 0.13 | 0.19 | 0.20 | 0.33 | |||
5 | 4.88 | 4.93 | 0.75% | 4.85 | 2.2% | 5.02 | 2.35% |
10 | 10.13 | 9.32 | 1.06% | 9.95 | 0.26% | 10.14 | 2.11% |
15 | 14.61 | 15.25 | 0.32% | 14.52 | 0.76% | 15.26 | 1.12% |
20 | 20.32 | 20.21 | 0.11% | 20.08 | 0.12% | 20.23 | 0.12% |
25 | 25.02 | 24.96 | 0.06% | 24.93 | 0.08% | 25.07 | 0.04% |
30 | 29.96 | 30.11 | 0.14% | 30.04 | 0.05% | 30.02 | 0.01% |
35 | 35.12 | 35.07 | 0.09% | 35.03 | 0.01% | 35.05 | 0.02% |
40 | 40.24 | 40.13 | 0.08% | 40.26 | 0.18% | 40.03 | 0.01% |
45 | 45.35 | 45.19 | 0.13% | 45.21 | 0.13% | 45.14 | 0.12% |
50 | 50.25 | 50.15 | 0.13% | 50.12 | 0.19% | 50.17 | 0.11% |
55 | 55.06 | 55.03 | 0.01% | 55.15 | 0.23% | 55.12 | 0.09% |
60 | 59.91 | 60.02 | 0.01% | 60.14 | 0.20% | 60.06 | 0.02% |
Position | Measurements in the x-Axis Direction | Measurements in the y-Axis Direction | Measurements in the z-Axis Direction | Synthesis Error | Error Correction Results | |||
---|---|---|---|---|---|---|---|---|
x-Axis Direction Error | y-Axis Direction Error | z-Axis Direction Error | Synthesis Error after Correction | |||||
0 | 26.50 | 42.45 | 0.00 | 0.344 | 0.025 | −0.016 | 0.035 | 0.043 |
20 | 49.42 | 55.65 | 0.04 | −0.404 | −0.065 | −0.039 | −0.075 | −0.179 |
40 | 42.04 | −56.91 | 0.03 | −0.213 | −0.092 | 0.001 | −0.009 | −0.100 |
60 | −39.47 | 55.12 | −0.03 | −0.347 | 0.024 | 0.061 | 0.064 | 0.149 |
80 | 10.17 | 41.46 | −0.04 | −0.232 | −0.009 | 0.051 | −0.034 | 0.008 |
100 | 47.17 | −25.63 | 0.03 | −0.343 | 0.072 | −0.063 | 0.016 | 0.026 |
120 | 23.31 | −46.86 | −0.06 | −0.308 | −0.060 | −0.036 | −0.024 | −0.120 |
140 | −48.87 | 23.59 | −0.03 | 0.022 | 0.018 | 0.072 | 0.026 | 0.116 |
160 | −9.06 | 49.00 | −0.07 | −0.371 | 0.031 | 0.049 | 0.093 | 0.173 |
180 | −34.47 | 3.39 | −0.09 | −0.331 | 0.045 | 0.042 | 0.080 | 0.167 |
200 | 38.60 | −15.25 | −0.09 | 0.247 | −0.098 | −0.051 | 0.056 | −0.093 |
220 | 41.16 | −54.55 | 0.03 | −0.236 | 0.085 | 0.012 | −0.010 | 0.087 |
240 | 22.61 | 2.53 | −0.08 | −0.146 | 0.064 | −0.065 | 0.080 | 0.079 |
260 | 45.58 | −41.30 | 0.05 | 0.113 | −0.084 | −0.049 | −0.026 | −0.159 |
280 | −58.62 | 40.45 | 0.02 | −0.313 | −0.092 | 0.049 | −0.013 | −0.056 |
300 | −32.48 | −45.63 | 0.05 | 0.400 | 0.028 | 0.014 | −0.002 | 0.041 |
320 | −1.69 | −22.75 | 0.10 | 0.268 | −0.068 | 0.069 | −0.026 | −0.024 |
340 | −24.19 | 43.16 | −0.03 | 0.177 | −0.080 | −0.066 | −0.053 | −0.199 |
Average value | 5.43 | 2.66 | −0.01 | −0.093 | −0.01 | 0.00 | 0.01 | −0.002 |
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Yang, Y.; Wang, M.; Wang, X.; Li, C.; Shang, Z.; Zhao, L. A Novel Monocular Vision Technique for the Detection of Electric Transmission Tower Tilting Trend. Appl. Sci. 2023, 13, 407. https://doi.org/10.3390/app13010407
Yang Y, Wang M, Wang X, Li C, Shang Z, Zhao L. A Novel Monocular Vision Technique for the Detection of Electric Transmission Tower Tilting Trend. Applied Sciences. 2023; 13(1):407. https://doi.org/10.3390/app13010407
Chicago/Turabian StyleYang, Yongsheng, Minzhen Wang, Xinheng Wang, Cheng Li, Ziwen Shang, and Liying Zhao. 2023. "A Novel Monocular Vision Technique for the Detection of Electric Transmission Tower Tilting Trend" Applied Sciences 13, no. 1: 407. https://doi.org/10.3390/app13010407
APA StyleYang, Y., Wang, M., Wang, X., Li, C., Shang, Z., & Zhao, L. (2023). A Novel Monocular Vision Technique for the Detection of Electric Transmission Tower Tilting Trend. Applied Sciences, 13(1), 407. https://doi.org/10.3390/app13010407