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
Semantic and Instance Segmentation of Room Features in Floor Plans using Mask R-CNN
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
2019 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Machine learning techniques within Computer Vision are rapidly improving computers' high-level understanding of images, thus revealing new opportunities to accomplish tasks that previously required manual intervention from humans. This paper aims to study where the current machine learning state-of-the-art is when it comes to parsing and segmenting bitmap images of floor plans. To assess the above, this paper evaluates one of the state-of-the-art models within instance segmentation, Mask R-CNN, on a size-limited and challenging floor plan dataset. The model handles detecting both objects and generating a high-quality segmentation map for each object, allowing for complete image segmentation using only a single network. Additionally, in order to extend the dataset, synthetic data generation was explored, and results indicate that it aids the network with floor plan generalization. The network is evaluated on both semantic and instance segmentation metrics and results show that the network yields good, almost completely segmented floor plans on smaller blueprints with little noise, while yielding decent but not completely segmented floor plans on large blueprints with a large amount of noise. Based on the results and them being achieved from a limited dataset, Mask R-CNN shows that it has potential in both accuracy and robustness for floor plans segmentation, either as a standalone network or alternatively as part of a pipeline of several methods and techniques.

Place, publisher, year, edition, pages
2019. , p. 55
Series
UPTEC IT, ISSN 1401-5749 ; 19011
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:uu:diva-393348OAI: oai:DiVA.org:uu-393348DiVA, id: diva2:1352780
Educational program
Master of Science Programme in Information Technology Engineering
Supervisors
Examiners
Available from: 2019-09-19 Created: 2019-09-19 Last updated: 2019-11-19Bibliographically approved

Open Access in DiVA

fulltext(8988 kB)26 downloads
File information
File name FULLTEXT01.pdfFile size 8988 kBChecksum SHA-512
51607359e61c8ef8afd9361cf1eb168946c1646fb98422a794be00772bf9c56dcdd0987d87a2d451dcd753b4163ed26b02ce5a2bbb17fbb65d28c981264eb57c
Type fulltextMimetype application/pdf

By organisation
Department of Information Technology
Engineering and Technology

Search outside of DiVA

GoogleGoogle Scholar
Total: 26 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: 131 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