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Semantic Segmentation of Iron Ore Pellets with Neural Networks
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

This master’s thesis evaluates five existing Convolutional Neural Network (CNN) models for semantic segmentation of optical microscopy images of iron ore pellets. The models are PSPNet, FC-DenseNet, DeepLabv3+, BiSeNet and GCN. The dataset used for training and evaluation contains 180 microscopy images of iron ore pellets collected from LKAB’s experimental blast furnace in Luleå, Sweden. This thesis also investigates the impact of the dataset size and data augmentation on performance. The best performing CNN model on the task was PSPNet, which had an average accuracy of 91.7% on the dataset. Simple data augmentation techniques, horizontal and vertical flipping, improved the models’ average accuracy performance with 3.4% on average. From the results in this thesis, it was concluded that there are benefits to using CNNs for analysis of iron ore pellets, with time-saving and improved analysis as the two notable areas.

Place, publisher, year, edition, pages
2019. , p. 70
Keywords [en]
Convolutional Neural Networks, Iron Ore Pellets, Semantic Segmentation
National Category
Other Engineering and Technologies not elsewhere specified
Identifiers
URN: urn:nbn:se:ltu:diva-74352OAI: oai:DiVA.org:ltu-74352DiVA, id: diva2:1322752
External cooperation
Data Ductus; LKAB
Subject / course
Student thesis, at least 30 credits
Educational program
Space Engineering, master's level
Presentation
(English)
Examiners
Available from: 2019-06-12 Created: 2019-06-11 Last updated: 2019-06-12Bibliographically approved

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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