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Neural Networks for Semantic Segmentation in the Food Packaging Industry
Linköping University, Department of Electrical Engineering, Computer Vision.
2018 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Industrial applications of computer vision often utilize traditional image processing techniques whereas state-of-the-art methods in most image processing challenges are almost exclusively based on convolutional neural networks (CNNs). Thus there is a large potential for improving the performance of many machine vision applications by incorporating CNNs.

One such application is the classification of juice boxes with straws, where the baseline solution uses classical image processing techniques on depth images to reject or accept juice boxes. This thesis aim to investigate how CNNs perform on the task of semantic segmentation (pixel-wise classification) of said images and if the result can be used to increase classification performance.

A drawback of CNNs is that they usually require large amounts of labelled data for training to be able to generalize and learn anything useful. As labelled data is hard to come by, two ways to get cheap data are investigated, one being synthetic data generation and the other being automatic labelling using the baseline solution.

The implemented network performs well on semantic segmentation, even when trained on synthetic data only, though the performance increases with the ratio of real (automatically labelled) to synthetic images. The classification task is very sensitive to small errors in semantic segmentation and the results are therefore not as good as the baseline solution. It is suspected that the drop in performance between validation and test data is due to a domain shift between the data sets, e.g. variations in data collection and straw and box type, and fine-tuning to the target domain could definitely increase performance.

When trained on synthetic data the domain shift is even larger and the performance on classification is next to useless. It is likely that the results could be improved by using more advanced data generation, e.g. a generative adversarial network (GAN), or more rigorous modelling of the data.

Place, publisher, year, edition, pages
2018. , p. 42
Keywords [en]
Deep learning, neural networks, semantic segmentation
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-145413ISRN: LiTH-ISY-EX--18/5113--SEOAI: oai:DiVA.org:liu-145413DiVA, id: diva2:1189501
External cooperation
Sick IVP, Linköping
Subject / course
Computer Vision Laboratory
Presentation
2018-02-28, Systemet, Linköping, 09:00 (Swedish)
Supervisors
Examiners
Available from: 2018-03-15 Created: 2018-03-11 Last updated: 2018-03-15Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
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  • vancouver
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Language
  • de-DE
  • en-GB
  • en-US
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  • nn-NB
  • sv-SE
  • Other locale
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Output format
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