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Fence surveillance with convolutional neural networks
Halmstad University, School of Information Technology.
Halmstad University, School of Information Technology.
2018 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

Broken fences is a big security risk for any facility or area with strict security standards. In this report we suggest a machine learning approach to automate the surveillance for chain-linked fences. The main challenge is to classify broken and non-broken fences with the help of a convolution neural network. Gathering data for this task is done by hand and the dataset is about 127 videos at 26 minutes length total on 23 different locations. The model and dataset are tested on three performances traits, scaling, augmentation improvement and false rate. In these tests we concluded that nearest neighbor increased accuracy. Classifying with fences that has been included in the training data a false rate that was low, about 1%. Classifying with fences that are unknown to the model produced a false rate of about 90%. With these results we concludes that this method and dataset is useful under the right circumstances but not in an unknown environment.

Place, publisher, year, edition, pages
2018.
Keywords [en]
Neural Network, Surveillence, Artificial Intelligence
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:hh:diva-37116OAI: oai:DiVA.org:hh-37116DiVA, id: diva2:1219617
External cooperation
RISE Viktoria
Educational program
Computer Science and Engineering, 300 credits
Examiners
Available from: 2018-06-19 Created: 2018-06-17 Last updated: 2018-06-19Bibliographically approved

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CiteExportLink to record
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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