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Classifying district heating network leakages in aerial thermal imagery
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Termisk Systemteknik AB Linköping, Sweden.ORCID iD: 0000-0002-6591-9400
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Termisk Systemteknik AB Linköping, Sweden.ORCID iD: 0000-0002-6763-5487
2014 (English)Conference paper, Oral presentation only (Other academic)
Abstract [en]

In this paper we address the problem of automatically detecting leakages in underground pipes of district heating networks from images captured by an airborne thermal camera. The basic idea is to classify each relevant image region as a leakage if its temperature exceeds a threshold. This simple approach yields a significant number of false positives. We propose to address this issue by machine learning techniques and provide extensive experimental analysis on real-world data. The results show that this postprocessing step significantly improves the usefulness of the system.

Place, publisher, year, edition, pages
2014.
Series
Svenska sällskapet för automatiserad bildanalys (SSBA)
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-127540OAI: oai:DiVA.org:liu-127540DiVA: diva2:925813
Conference
Swedish Symposium on Image Analysis
Available from: 2016-05-03 Created: 2016-05-03 Last updated: 2016-06-10Bibliographically approved

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fulltext(3700 kB)75 downloads
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Type fulltextMimetype application/pdf

Authority records BETA

Berg, AmandaAhlberg, Jörgen

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • 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