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Automatic Power Line Inspection Using UAV Images
Open accessAbstract
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.
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Cites
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
Cited by
Towards the Unmanned Aerial Vehicles (UAVs): A Comprehensive Review
Mohsan, S. A. H., Khan, M. A., Noor, F., Ullah, I., & Alsharif, M. H. (2022) · Drones · doi:10.3390/drones6060147 · Cited by 916
Shakeri, R., Al-Garadi, M. A., Badawy, A., Mohamed, A., Khattab, T., Al-Ali, A. K., Harras, K. A., & Guizani, M. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2924143 · Cited by 295
Metadata
- Authors
- Zhang, Y., Yuan, X., Li, W., & Chen, S.
- Venue
- Remote Sensing
- Publisher
- MDPI AG
- Published
- 2017-08-10
- Volume
- 9
- Issue
- 8
- Pages
- 824
- ISSN
- 2072-4292 (ISSN-L)
- OpenAlex
- W2743056029
- Cited by
- 193
- References
- 1
- FWCI
- 10.13
- Type
- journal article
- Language
- en
- Open access
- gold
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