2018
DOI: 10.1016/j.autcon.2018.09.014
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Automated detection and decomposition of railway tunnels from Mobile Laser Scanning Datasets

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Cited by 61 publications
(58 citation statements)
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“…In the reference [17], the track detection method based on section histogram and peak detection had fewer errors than the aforementioned template matching, but it still cannot solve the problem in tilted ground and track network. The method proposed in literature was based on peak detection and machine learning method SVM to further improve the accuracy of detection [18]. The region growing method proposed in this paper effectively solves the problem of track extraction in these two environments.…”
Section: Resultsmentioning
confidence: 99%
“…In the reference [17], the track detection method based on section histogram and peak detection had fewer errors than the aforementioned template matching, but it still cannot solve the problem in tilted ground and track network. The method proposed in literature was based on peak detection and machine learning method SVM to further improve the accuracy of detection [18]. The region growing method proposed in this paper effectively solves the problem of track extraction in these two environments.…”
Section: Resultsmentioning
confidence: 99%
“…This paper follows the steps previously set in [20], where a methodology for the automatic classification of railway tunnel elements from MLS datasets was presented. The present work is oriented to the development and analysis of a new system for the recognition, classification, and inspection of electrical railway infrastructures using LiDAR data.…”
Section: Methodsmentioning
confidence: 99%
“…The work starts with a labelled point cloud divided into lining, ground, railway tracks, overhead line, and cantilevers. This information is extracted following the steps presented in [20], and as reflected in Figure 1. In this section, the method developed for the automatic inspection of railway tunnels' power line is presented.…”
Section: Methodsmentioning
confidence: 99%
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