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Semantic segmentation of seabed sonar imagery using deep learning
Linköping University, Department of Computer and Information Science, Software and Systems.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Semantisk segmentering av sonarbilder från havsbotten med deep learning (Swedish)
Abstract [en]

For investigating the large parts of the ocean which have yet to be mapped, there is a need for autonomous underwater vehicles. Current state-of-the-art underwater positioning often relies on external data from other vessels or beacons. Processing seabed image data could potentially improve autonomy for underwater vehicles.

In this thesis, image data from a synthetic aperture sonar (SAS) was manually segmented into two classes: sand and gravel. Two different convolutional neural networks (CNN) were trained using different loss functions, and the results were examined. The best performing network, U-Net trained with the IoU loss function, achieved dice coefficient and IoU scores of 0.645 and 0.476, respectively. It was concluded that CNNs are a viable approach for segmenting SAS image data, but there is much room for improvement.

Place, publisher, year, edition, pages
2019. , p. 29
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:liu:diva-160561ISRN: LIU-IDA/LITH-EX-A--19/073--SEOAI: oai:DiVA.org:liu-160561DiVA, id: diva2:1355367
External cooperation
Saab Dynamics
Subject / course
Computer Engineering
Presentation
2019-08-30, Alan Turing, Linköpings universitet, 10:00 (English)
Supervisors
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Available from: 2019-10-04 Created: 2019-09-27 Last updated: 2019-10-04Bibliographically approved

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4344454647484946 of 164
CiteExportLink to record
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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