du.sePublikationer
Ändra sökning
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • chicago-author-date
  • chicago-note-bibliography
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Automating condition monitoring of vegetation on railway trackbeds and embankments
Högskolan Dalarna, Akademin Industri och samhälle, Informatik. Edinburgh Napier University.ORCID-id: 0000-0003-4812-4988
2016 (Engelska)Doktorsavhandling, monografi (Övrigt vetenskapligt)
Abstract [en]

Vegetation growing on railway trackbeds and embankments present potential problems. The presence of vegetation threatens the safety of personnel inspecting the railway infrastructure. In addition vegetation growth clogs the ballast and results in inadequate track drainage which in turn could lead to the collapse of the railway embankment.

Assessing vegetation within the realm of railway maintenance is mainly carried out manually by making visual inspections along the track. This is done either on-site or by watching videos recorded by maintenance vehicles mainly operated by the national railway administrative body.

A need for the automated detection and characterisation of vegetation on railways (a subset of vegetation control/management) has been identified in collaboration with local railway maintenance subcontractors and Trafikverket, the Swedish Transport Administration (STA). The latter is responsible for long-term planning of the transport system for all types of traffic, as well as for the building, operation and maintenance of public roads and railways.

The purpose of this research project was to investigate how vegetation can be measured and quantified by human raters and how machine vision can automate the same process.

Data were acquired at railway trackbeds and embankments during field measurement experiments. All field data (such as images) in this thesis work was acquired on operational, lightly trafficked railway tracks, mostly trafficked by goods trains. Data were also generated by letting (human) raters conduct visual estimates of plant cover and/or count the number of plants, either on-site or in-house by making visual estimates of the images acquired from the field experiments. Later, the degree of reliability of(human) raters’ visual estimates were investigated and compared against machine vision algorithms.

The overall results of the investigations involving human raters showed inconsistency in their estimates, and are therefore unreliable. As a result of the exploration of machine vision, computational methods and algorithms enabling automatic detection and characterisation of vegetation along railways were developed. The results achieved in the current work have shown that the use of image data for detecting vegetation is indeed possible and that such results could form the base for decisions regarding vegetation control. The performance of the machine vision algorithm which quantifies the vegetation cover was able to process 98% of the im-age data. Investigations of classifying plants from images were conducted in in order to recognise the specie. The classification rate accuracy was 95%.Objective measurements such as the ones proposed in thesis offers easy access to the measurements to all the involved parties and makes the subcontracting process easier i.e., both the subcontractors and the national railway administration are given the same reference framework concerning vegetation before signing a contract, which can then be crosschecked post maintenance.A very important issue which comes with an increasing ability to recognise species is the maintenance of biological diversity. Biological diversity along the trackbeds and embankments can be mapped, and maintained, through better and robust monitoring procedures. Continuously monitoring the state of vegetation along railways is highly recommended in order to identify a need for maintenance actions, and in addition to keep track of biodiversity. The computational methods or algorithms developed form the foundation of an automatic inspection system capable of objectively supporting manual inspections, or replacing manual inspections.

Ort, förlag, år, upplaga, sidor
Edinburgh University Press, 2016. , s. 301
Nationell ämneskategori
Data- och informationsvetenskap
Forskningsämne
Komplexa system - mikrodataanalys
Identifikatorer
URN: urn:nbn:se:du-21465OAI: oai:DiVA.org:du-21465DiVA, id: diva2:929179
Disputation
2015-10-15, C96 in Napiers' Tower, Merchiston Campus. 10 Colinton Road. Edinburgh. EH10 5DT, Edinburgh, Scotland, UK, 14:00 (Engelska)
Opponent
Handledare
Forskningsfinansiär
TrafikverketTillgänglig från: 2016-05-20 Skapad: 2016-05-17 Senast uppdaterad: 2018-01-10Bibliografiskt granskad

Open Access i DiVA

fulltext(24759 kB)350 nedladdningar
Filinformation
Filnamn FULLTEXT01.pdfFilstorlek 24759 kBChecksumma SHA-512
d47acce579d9e95f9e29e890410541fe088eea4d42dffdba28a56383c46e5635df1a674524c023f5564fdc5def6724b5a532c72aba81fd2bc96e685a979989ea
Typ fulltextMimetyp application/pdf

Personposter BETA

Nyberg, Roger G.

Sök vidare i DiVA

Av författaren/redaktören
Nyberg, Roger G.
Av organisationen
Informatik
Data- och informationsvetenskap

Sök vidare utanför DiVA

GoogleGoogle Scholar
Totalt: 350 nedladdningar
Antalet nedladdningar är summan av nedladdningar för alla fulltexter. Det kan inkludera t.ex tidigare versioner som nu inte längre är tillgängliga.

urn-nbn

Altmetricpoäng

urn-nbn
Totalt: 1297 träffar
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • chicago-author-date
  • chicago-note-bibliography
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf