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Pattern recognition for classifying the condition of wooden railway sleepers
Dalarna University, School of Technology and Business Studies, Computer Engineering.
Dalarna University, School of Technology and Business Studies, Computer Engineering.
Dalarna University, School of Technology and Business Studies, Computer Engineering.
2010 (English)In: Multimedia Computing and Information Technology (MCIT), 2010 International Conference on Multimedia Computing and Information Technology, Sharjah, 2010, 61-64 p.Conference paper, Published paper (Refereed)
Abstract [en]

This paper summarises the results of using a pattern recognition approach for classifying the condition of wooden railway sleepers. Railway sleeper inspections are currently done manually; visual inspection being the most common approach, with some deeper examination using an axe to judge the condition. Digital images of the sleepers were acquired to compensate for the human visual capabilities. Appropriate image analysis techniques were applied to further process the images and necessary features such as number of cracks, crack length etc have been extracted. Finally a pattern recognition and classification approach has been adopted to further classify the condition of the sleeper into classes (good or bad). A Support Vector Machine (SVM) using a Gaussian kernel has achieved good classification rate (86%) in the current case.

Place, publisher, year, edition, pages
Sharjah, 2010. 61-64 p.
National Category
Computer Engineering
Research subject
Complex Systems – Microdata Analysis, Automatisk inspektion av järnvägsslipers
Identifiers
URN: urn:nbn:se:du-6033DOI: 10.1109/MCIT.2010.5444850OAI: oai:dalea.du.se:6033DiVA: diva2:522442
Conference
IEEE MCIT, Sharjah, 2-4 March, 2010
Available from: 2011-11-01 Created: 2011-11-01 Last updated: 2015-12-08Bibliographically approved

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • chicago-author-date
  • chicago-note-bibliography
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf