Power Line Extraction and Tree Risk Detection Based on Airborne LiDAR
Transmission lines are the basis of human production and activities. In order to ensure their safe operation, it is essential to regularly conduct transmission line inspections and identify tree risk in a timely manner. In this paper, a power line extraction and tree risk detection method is propose...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/19/8233 |
| _version_ | 1797575189797535744 |
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| author | Siyuan Xi Zhaojiang Zhang Yufen Niu Huirong Li Qiang Zhang |
| author_facet | Siyuan Xi Zhaojiang Zhang Yufen Niu Huirong Li Qiang Zhang |
| author_sort | Siyuan Xi |
| collection | DOAJ |
| description | Transmission lines are the basis of human production and activities. In order to ensure their safe operation, it is essential to regularly conduct transmission line inspections and identify tree risk in a timely manner. In this paper, a power line extraction and tree risk detection method is proposed. Firstly, the height difference and local dimension feature probability model are used to extract power line points, and then the Cloth Simulation Filter algorithm and neighborhood sharing method are creatively introduced to distinguish conductors and ground wires. Secondly, conductor reconstruction is realized by the approach of the linear–catenary model, and numerous non-risk points are excluded by constructing the tree risk point candidate area centered on the conductor’s reconstruction curve. Finally, the grading strategy for the safety distance calculation is used to detect the tree risk points. The experimental results show that the precision, recall, and F-score of the conductors (ground wires) classification exceed 98.05% (97.98%), 99.00% (99.14%), and 98.58% (98.56%), respectively, which presents a high classification accuracy. The Root-Mean-Square Error, Maximum Error, and Minimum Error of the conductor’s reconstruction are better than 3.67 cm, 7.13 cm, and 2.64 cm, respectively, and the Mean Absolute Error of the safety distance calculation is better than 6.47 cm, proving the effectiveness and rationality of the proposed tree risk points detection method. |
| first_indexed | 2024-03-10T21:35:03Z |
| format | Article |
| id | doaj.art-d205c6a7881544629cfa105bec09577b |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T21:35:03Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-d205c6a7881544629cfa105bec09577b2023-11-19T15:04:37ZengMDPI AGSensors1424-82202023-10-012319823310.3390/s23198233Power Line Extraction and Tree Risk Detection Based on Airborne LiDARSiyuan Xi0Zhaojiang Zhang1Yufen Niu2Huirong Li3Qiang Zhang4School of Mining and Geomatics Engineering, Hebei University of Engineering, Handan 056038, ChinaSchool of Mining and Geomatics Engineering, Hebei University of Engineering, Handan 056038, ChinaSchool of Mining and Geomatics Engineering, Hebei University of Engineering, Handan 056038, ChinaSchool of Mining and Geomatics Engineering, Hebei University of Engineering, Handan 056038, ChinaSchool of Mining and Geomatics Engineering, Hebei University of Engineering, Handan 056038, ChinaTransmission lines are the basis of human production and activities. In order to ensure their safe operation, it is essential to regularly conduct transmission line inspections and identify tree risk in a timely manner. In this paper, a power line extraction and tree risk detection method is proposed. Firstly, the height difference and local dimension feature probability model are used to extract power line points, and then the Cloth Simulation Filter algorithm and neighborhood sharing method are creatively introduced to distinguish conductors and ground wires. Secondly, conductor reconstruction is realized by the approach of the linear–catenary model, and numerous non-risk points are excluded by constructing the tree risk point candidate area centered on the conductor’s reconstruction curve. Finally, the grading strategy for the safety distance calculation is used to detect the tree risk points. The experimental results show that the precision, recall, and F-score of the conductors (ground wires) classification exceed 98.05% (97.98%), 99.00% (99.14%), and 98.58% (98.56%), respectively, which presents a high classification accuracy. The Root-Mean-Square Error, Maximum Error, and Minimum Error of the conductor’s reconstruction are better than 3.67 cm, 7.13 cm, and 2.64 cm, respectively, and the Mean Absolute Error of the safety distance calculation is better than 6.47 cm, proving the effectiveness and rationality of the proposed tree risk points detection method.https://www.mdpi.com/1424-8220/23/19/8233power line extractionsafety distance calculationpoint cloudtransmission line inspection |
| spellingShingle | Siyuan Xi Zhaojiang Zhang Yufen Niu Huirong Li Qiang Zhang Power Line Extraction and Tree Risk Detection Based on Airborne LiDAR Sensors power line extraction safety distance calculation point cloud transmission line inspection |
| title | Power Line Extraction and Tree Risk Detection Based on Airborne LiDAR |
| title_full | Power Line Extraction and Tree Risk Detection Based on Airborne LiDAR |
| title_fullStr | Power Line Extraction and Tree Risk Detection Based on Airborne LiDAR |
| title_full_unstemmed | Power Line Extraction and Tree Risk Detection Based on Airborne LiDAR |
| title_short | Power Line Extraction and Tree Risk Detection Based on Airborne LiDAR |
| title_sort | power line extraction and tree risk detection based on airborne lidar |
| topic | power line extraction safety distance calculation point cloud transmission line inspection |
| url | https://www.mdpi.com/1424-8220/23/19/8233 |
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