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Detecting Slag Formation with Deep Learning Methods: An experimental study of different deep learning image segmentation models
Linköping University, Department of Electrical Engineering, Computer Vision.
Linköping University, Department of Electrical Engineering, Computer Vision.
2021 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Image segmentation through neural networks and deep learning have, in the recent decade, become a successful tool for automated decision-making. For Luossavaara-Kiirunavaara Aktiebolag (LKAB), this means identifying the amount of slag inside a furnace through computer vision. 

There are many prominent convolutional neural network architectures in the literature, and this thesis explores two: a modified U-Net and the PSPNet. The architectures were combined with three loss functions and three class weighting schemes resulting in 18 model configurations that were evaluated and compared. This thesis also explores transfer learning techniques for neural networks tasked with identifying slag in images from inside a furnace. The benefit of transfer learning is that the network can learn to find features from already labeled data of another context. Finally, the thesis explored how temporal information could be utilised by adding an LSTM layer to a model taking pairs of images as input, instead of one.

The results show (1) that the PSPNet outperformed the U-Net for all tested configurations in all relevant metrics, (2) that the model is able to find more complex features while converging quicker by using transfer learning, and (3) that utilising temporal information reduced the variance of the predictions, and that the modified PSPNet using an LSTM layer showed promise in handling images with outlying characteristics. 

Place, publisher, year, edition, pages
2021. , p. 78
Keywords [en]
deep learning, deep neural network, computer vision, image segmentation, iron ore pelletising plant, furnace slag-detection, U-Net, PSPNet
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-177269ISRN: LiTH-ISY-EX--21/5427--SEOAI: oai:DiVA.org:liu-177269DiVA, id: diva2:1572304
External cooperation
Combient Mix; LKAB
Subject / course
Computer Engineering
Presentation
2021-06-15, 10:15 (English)
Supervisors
Examiners
Available from: 2021-06-24 Created: 2021-06-23 Last updated: 2021-06-24Bibliographically approved

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fulltext(14788 kB)705 downloads
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CiteExportLink to record
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Citation style
  • apa
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Output format
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