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Automated Underwater Pipeline Damage Detection using Neural Nets
KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.
KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.
KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.
KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL. (RPL/EECS)ORCID iD: 0000-0002-7796-1438
2019 (English)Conference paper, Oral presentation only (Refereed)
Abstract [en]

Pipeline inspection is a very human intensive taskand automation could improve efficiencies significantly. We propose a system that could allow an autonomous underwater vehicle (AUV), to detect pipeline damage in a stream of images.Our classifiers were based on transfer learning from pre-trained convolutional neural networks (CNN). This allows us to achieve good results despite relatively few training examples of damage. We test the approach using data from an actual pipeline inspection.

Place, publisher, year, edition, pages
2019.
National Category
Computer Systems Robotics
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-256311OAI: oai:DiVA.org:kth-256311DiVA, id: diva2:1344503
Conference
ICRA 2019 Workshop on Underwater Robotics Perception
Funder
Swedish Foundation for Strategic Research , IRC15-0046
Note

QC 20190827

Available from: 2019-08-21 Created: 2019-08-21 Last updated: 2019-08-27Bibliographically approved

Open Access in DiVA

fulltext(1223 kB)26 downloads
File information
File name FULLTEXT01.pdfFile size 1223 kBChecksum SHA-512
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Type fulltextMimetype application/pdf

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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
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Output format
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