Automatic Power Line Inspection Using UAV Images
Power line inspection ensures the safe operation of a power transmission grid. Using unmanned aerial vehicle (UAV) images of power line corridors is an effective way to carry out these vital inspections. In this paper, we propose an automatic inspection method for power lines using UAV images. This...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-08-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/9/8/824 |
| _version_ | 1828438603892523008 |
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| author | Yong Zhang Xiuxiao Yuan Wenzhuo Li Shiyu Chen |
| author_facet | Yong Zhang Xiuxiao Yuan Wenzhuo Li Shiyu Chen |
| author_sort | Yong Zhang |
| collection | DOAJ |
| description | Power line inspection ensures the safe operation of a power transmission grid. Using unmanned aerial vehicle (UAV) images of power line corridors is an effective way to carry out these vital inspections. In this paper, we propose an automatic inspection method for power lines using UAV images. This method, known as the power line automatic measurement method based on epipolar constraints (PLAMEC), acquires the spatial position of the power lines. Then, the semi patch matching based on epipolar constraints (SPMEC) dense matching method is applied to automatically extract dense point clouds within the power line corridor. Obstacles can then be automatically detected by calculating the spatial distance between a power line and the point cloud representing the ground. Experimental results show that the PLAMEC automatically measures power lines effectively with a measurement accuracy consistent with that of manual stereo measurements. The height root mean square (RMS) error of the point cloud was 0.233 m, and the RMS error of the power line was 0.205 m. In addition, we verified the detected obstacles in the field and measured the distance between the canopy and power line using a laser range finder. The results show that the difference of these two distances was within ±0.5 m. |
| first_indexed | 2024-12-10T20:07:06Z |
| format | Article |
| id | doaj.art-1b19248c43ca4a45a88c1dc736afde98 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-10T20:07:06Z |
| publishDate | 2017-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-1b19248c43ca4a45a88c1dc736afde982022-12-22T01:35:22ZengMDPI AGRemote Sensing2072-42922017-08-019882410.3390/rs9080824rs9080824Automatic Power Line Inspection Using UAV ImagesYong Zhang0Xiuxiao Yuan1Wenzhuo Li2Shiyu Chen3School of Remote Sensing and Information Engineering, Wuhan University, 129 Luoyu Road, Wuhan 430079, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, 129 Luoyu Road, Wuhan 430079, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, 129 Luoyu Road, Wuhan 430079, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, 129 Luoyu Road, Wuhan 430079, ChinaPower line inspection ensures the safe operation of a power transmission grid. Using unmanned aerial vehicle (UAV) images of power line corridors is an effective way to carry out these vital inspections. In this paper, we propose an automatic inspection method for power lines using UAV images. This method, known as the power line automatic measurement method based on epipolar constraints (PLAMEC), acquires the spatial position of the power lines. Then, the semi patch matching based on epipolar constraints (SPMEC) dense matching method is applied to automatically extract dense point clouds within the power line corridor. Obstacles can then be automatically detected by calculating the spatial distance between a power line and the point cloud representing the ground. Experimental results show that the PLAMEC automatically measures power lines effectively with a measurement accuracy consistent with that of manual stereo measurements. The height root mean square (RMS) error of the point cloud was 0.233 m, and the RMS error of the power line was 0.205 m. In addition, we verified the detected obstacles in the field and measured the distance between the canopy and power line using a laser range finder. The results show that the difference of these two distances was within ±0.5 m.https://www.mdpi.com/2072-4292/9/8/824power line inspectionpower line extractiondense matchingobstacles within power line corridorspower line monitoring |
| spellingShingle | Yong Zhang Xiuxiao Yuan Wenzhuo Li Shiyu Chen Automatic Power Line Inspection Using UAV Images Remote Sensing power line inspection power line extraction dense matching obstacles within power line corridors power line monitoring |
| title | Automatic Power Line Inspection Using UAV Images |
| title_full | Automatic Power Line Inspection Using UAV Images |
| title_fullStr | Automatic Power Line Inspection Using UAV Images |
| title_full_unstemmed | Automatic Power Line Inspection Using UAV Images |
| title_short | Automatic Power Line Inspection Using UAV Images |
| title_sort | automatic power line inspection using uav images |
| topic | power line inspection power line extraction dense matching obstacles within power line corridors power line monitoring |
| url | https://www.mdpi.com/2072-4292/9/8/824 |
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