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Reading Barcodes with Neural Networks
Linköping University, Department of Electrical Engineering, Computer Vision.
2017 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Barcodes are ubiquituous in modern society and they have had industrial application for decades. However, for noisy images modern methods can underperform. Poor lighting conditions, occlusions and low resolution can be problematic in decoding. This thesis aims to solve this problem by using neural networks, which have enjoyed great success in many computer vision competitions the last years. We investigate how three different networks perform on data sets with noisy images. The first network is a single classifier, the second network is an ensemble classifier and the third is based on a pre-trained feature extractor. For comparison, we also test two baseline methods that are used in industry today. We generate training data using software and modify it to ensure proper generalization. Testing data is created by photographing barcodes in different settings, creating six image classes - normal, dark, white, rotated, occluded and wrinkled. The proposed single classifier and ensemble classifier outperform the baseline as well as the pre-trained feature extractor by a large margin. The thesis work was performed at SICK IVP, a machine vision company in Linköping in 2017.

Place, publisher, year, edition, pages
2017. , p. 37
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-143477ISRN: LiTH-ISY-EX--17/5102--SEOAI: oai:DiVA.org:liu-143477DiVA, id: diva2:1164104
External cooperation
SICK IVP
Subject / course
Electrical Engineering
Supervisors
Examiners
Available from: 2017-12-11 Created: 2017-12-09 Last updated: 2018-01-13Bibliographically approved

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • 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