Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • 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
Localising and Reconstructing Drill Holes in 3D Objects using Machine Learning
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
2018 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

To move towards an increasingly automated future, machines are becoming smarter and can now solve tasks only humans could before. In this thesis project, the possibility to find, classify and reconstruct holes in three dimensional objects using machine learning is explored. To achieve this, three dimensional convolutional neural networks are used as a method for semantic segmentation. To combat limited GPU memory and training time, a region-based network was created, this network used smaller regions of the 3D objects to process the image in parts, and thereby evade the memory barrier of the GPU, create reconstructions with a higher resolution, and lower training time.

The results show that 3D semantic segmentation is possible and is a promising method for reconstruction of features in 3D objects. However, the thesis work also highlights the importance of a qualitative dataset that is a good representation of the data that is intended to be used with the models.

Place, publisher, year, edition, pages
2018. , p. 50
Series
UPTEC IT, ISSN 1401-5749 ; 18009
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:uu:diva-355675OAI: oai:DiVA.org:uu-355675DiVA, id: diva2:1230313
Educational program
Master of Science Programme in Information Technology Engineering
Supervisors
Examiners
Available from: 2018-07-03 Created: 2018-07-03 Last updated: 2018-07-03Bibliographically approved

Open Access in DiVA

fulltext(1726 kB)27 downloads
File information
File name FULLTEXT01.pdfFile size 1726 kBChecksum SHA-512
9c4209901657552f2e4b752a5a65a070cb023c801def284681e24c752f7eaa5ed7527fbd946cb837519a1045bf7e91c7fb2d99513a348a955f5d09946a32f90d
Type fulltextMimetype application/pdf

By organisation
Department of Information Technology
Engineering and Technology

Search outside of DiVA

GoogleGoogle Scholar
Total: 27 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 62 hits
CiteExportLink to record
Permanent link

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