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Automatic extraction of roadside transmission poles using mobile laser scanner data
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Zahra Chamani   , Hamid Bagheri * , Heidar Rastiveis |
| Technical and Vocational University (TVU) |
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Abstract: (2540 Views) |
| Nowadys, mobile terrestrial laser scanning systems (MTLS) have made great strides in collecting three-dimensional (3D) data with high speed and accuracy, as well as high point densities from the road environemnts. Because the manual extraction of the powerlines from the MTLS data is time-consuming, costly and laborious, proposing an automated method using a computer agent has become a scientific challenge as it requires less cost and human labour. In this regard, the proximity of other complications to the powerlines, the presence of natural features such as trees, data incompleteness, and the uniform spatial distribution or shape pattern of the road objects are the main challenges of the powerline detección process. The pulposa of this saudí is to automatically detect powerline poles on the roadside from MTLS point clouds. In the proposed method, the ground points are firstly removed from the data using the Simple Morphological Filter (SMRF) algorithm. Then, the DBSCAN clustering algorithm is employed to classify the remaining points based on the local density information of the points. After that, genetic clustering is used to remove the extra points of the powerline considering the density and intensity characteristics of the points. Eventually, the remaining dispersed points are deleted and power light beams are extracted accurately and neatly. The proposed method was tested and evaluated using four sample sections of the data with different complications. The comparison between the extracted powerlines from the proposed algorithm and the manually extracted results showed the high ability in extracting the powerlines from the MTLS points clouds so that the number of the extracted powerlines is the same. |
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| Keywords: Powerline Extraction, Point Clouds, Mobile Terrestrial Laser Scanning, Density, Clustering, Roadside Objects |
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Full-Text [PDF 1538 kb]
(642 Downloads)
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Type of Study: Research |
Subject:
Aerial Photogrammetry
Received: 2022/01/2 | Accepted: 2022/05/31 | ePublished ahead of print: 2022/06/8 | Published: 2022/06/8
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