Pierluigi Amodio , Marcello De Giosa , Felice Iavernaro , Roberto La Scala , Arcangelo Labianca , Monica Lazzo , Francesca Mazzia , Lorenzo Pisani
{"title":"Detection of anomalies in the proximity of a railway line: A case study","authors":"Pierluigi Amodio , Marcello De Giosa , Felice Iavernaro , Roberto La Scala , Arcangelo Labianca , Monica Lazzo , Francesca Mazzia , Lorenzo Pisani","doi":"10.1016/j.jcmds.2022.100052","DOIUrl":null,"url":null,"abstract":"<div><p>A point cloud describing a railway environment is considered in a case study aimed at presenting a workflow for the automatic detection of external objects that, coming too close to the railway infrastructure, may cause potential risks for its correct functioning. The approach combines classical semantic segmentation methodologies with a novel geometric and numerical procedure to define a <em>region of interest</em>, consisting of a lower tube enveloping the 3D space occupied by the train during its transit and an upper tube enclosing the overhead contact lines. One useful application could be automatic vegetation monitoring in the proximity of the railway structure, which would help with planning maintenance pruning activities.</p></div>","PeriodicalId":100768,"journal":{"name":"Journal of Computational Mathematics and Data Science","volume":"4 ","pages":"Article 100052"},"PeriodicalIF":0.0000,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772415822000165/pdfft?md5=39ce7dbb7fdd23f164ad540509765339&pid=1-s2.0-S2772415822000165-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Computational Mathematics and Data Science","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2772415822000165","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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Abstract
A point cloud describing a railway environment is considered in a case study aimed at presenting a workflow for the automatic detection of external objects that, coming too close to the railway infrastructure, may cause potential risks for its correct functioning. The approach combines classical semantic segmentation methodologies with a novel geometric and numerical procedure to define a region of interest, consisting of a lower tube enveloping the 3D space occupied by the train during its transit and an upper tube enclosing the overhead contact lines. One useful application could be automatic vegetation monitoring in the proximity of the railway structure, which would help with planning maintenance pruning activities.
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