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Detecting Inspection Objects of Power Line from Cable Inspection Robot LiDAR Data
Power lines are extending to complex environments (e.g., lakes and forests), and the distribution of power lines in a tower is becoming complicated (e.g., multi-loop and multi-bundle). Additionally, power line inspection is becoming heavier and more difficult. Advanced LiDAR technology is increasing...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5948554/ https://www.ncbi.nlm.nih.gov/pubmed/29690560 http://dx.doi.org/10.3390/s18041284 |
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author | Qin, Xinyan Wu, Gongping Lei, Jin Fan, Fei Ye, Xuhui |
author_facet | Qin, Xinyan Wu, Gongping Lei, Jin Fan, Fei Ye, Xuhui |
author_sort | Qin, Xinyan |
collection | PubMed |
description | Power lines are extending to complex environments (e.g., lakes and forests), and the distribution of power lines in a tower is becoming complicated (e.g., multi-loop and multi-bundle). Additionally, power line inspection is becoming heavier and more difficult. Advanced LiDAR technology is increasingly being used to solve these difficulties. Based on precise cable inspection robot (CIR) LiDAR data and the distinctive position and orientation system (POS) data, we propose a novel methodology to detect inspection objects surrounding power lines. The proposed method mainly includes four steps: firstly, the original point cloud is divided into single-span data as a processing unit; secondly, the optimal elevation threshold is constructed to remove ground points without the existing filtering algorithm, improving data processing efficiency and extraction accuracy; thirdly, a single power line and its surrounding data can be respectively extracted by a structured partition based on a POS data (SPPD) algorithm from “layer” to “block” according to power line distribution; finally, a partition recognition method is proposed based on the distribution characteristics of inspection objects, highlighting the feature information and improving the recognition effect. The local neighborhood statistics and the 3D region growing method are used to recognize different inspection objects surrounding power lines in a partition. Three datasets were collected by two CIR LIDAR systems in our study. The experimental results demonstrate that an average 90.6% accuracy and average 98.2% precision at the point cloud level can be achieved. The successful extraction indicates that the proposed method is feasible and promising. Our study can be used to obtain precise dimensions of fittings for modeling, as well as automatic detection and location of security risks, so as to improve the intelligence level of power line inspection. |
format | Online Article Text |
id | pubmed-5948554 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-59485542018-05-17 Detecting Inspection Objects of Power Line from Cable Inspection Robot LiDAR Data Qin, Xinyan Wu, Gongping Lei, Jin Fan, Fei Ye, Xuhui Sensors (Basel) Article Power lines are extending to complex environments (e.g., lakes and forests), and the distribution of power lines in a tower is becoming complicated (e.g., multi-loop and multi-bundle). Additionally, power line inspection is becoming heavier and more difficult. Advanced LiDAR technology is increasingly being used to solve these difficulties. Based on precise cable inspection robot (CIR) LiDAR data and the distinctive position and orientation system (POS) data, we propose a novel methodology to detect inspection objects surrounding power lines. The proposed method mainly includes four steps: firstly, the original point cloud is divided into single-span data as a processing unit; secondly, the optimal elevation threshold is constructed to remove ground points without the existing filtering algorithm, improving data processing efficiency and extraction accuracy; thirdly, a single power line and its surrounding data can be respectively extracted by a structured partition based on a POS data (SPPD) algorithm from “layer” to “block” according to power line distribution; finally, a partition recognition method is proposed based on the distribution characteristics of inspection objects, highlighting the feature information and improving the recognition effect. The local neighborhood statistics and the 3D region growing method are used to recognize different inspection objects surrounding power lines in a partition. Three datasets were collected by two CIR LIDAR systems in our study. The experimental results demonstrate that an average 90.6% accuracy and average 98.2% precision at the point cloud level can be achieved. The successful extraction indicates that the proposed method is feasible and promising. Our study can be used to obtain precise dimensions of fittings for modeling, as well as automatic detection and location of security risks, so as to improve the intelligence level of power line inspection. MDPI 2018-04-22 /pmc/articles/PMC5948554/ /pubmed/29690560 http://dx.doi.org/10.3390/s18041284 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Qin, Xinyan Wu, Gongping Lei, Jin Fan, Fei Ye, Xuhui Detecting Inspection Objects of Power Line from Cable Inspection Robot LiDAR Data |
title | Detecting Inspection Objects of Power Line from Cable Inspection Robot LiDAR Data |
title_full | Detecting Inspection Objects of Power Line from Cable Inspection Robot LiDAR Data |
title_fullStr | Detecting Inspection Objects of Power Line from Cable Inspection Robot LiDAR Data |
title_full_unstemmed | Detecting Inspection Objects of Power Line from Cable Inspection Robot LiDAR Data |
title_short | Detecting Inspection Objects of Power Line from Cable Inspection Robot LiDAR Data |
title_sort | detecting inspection objects of power line from cable inspection robot lidar data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5948554/ https://www.ncbi.nlm.nih.gov/pubmed/29690560 http://dx.doi.org/10.3390/s18041284 |
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