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A Client-Server Solution for Detecting Guns in School Environment using Deep Learning Techniques
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
2019 (English)Independent thesis Advanced level (degree of Master (One Year)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

With the progress of deep learning methods the last couple of years, object detection related tasks are improving rapidly. Using object detection for detecting guns in schools remove the need for human supervision and hopefully reduces police response time. This paper investigates how a gun detection system can be built by reading frames locally and using a server for detection. The detector is based on a pre-trained SSD model and through transfer learning is taught to recognize guns. The detector obtained an Average Precision of 51.1% and the server response time for a frame of size 1920 x 1080 was 480 ms, but could be scaled down to 240 x 135 to reach 210 ms, without affecting the accuracy. A non-gun class was implemented to reduce the number of false positives and on a set of 300 images containing 165 guns, the number of false positives dropped from 21 to 11.

Place, publisher, year, edition, pages
2019. , p. 38
Keywords [en]
machine learning, gun detection, tensorflow, server, client, training, school shootings, school environment, gun control, SSD, RetinaNet, transfer learning, AI, surveillance, IP, network, camera, Axis, Communications
National Category
Media and Communication Technology
Identifiers
URN: urn:nbn:se:liu:diva-162476ISRN: LIU-ITN-TEK-A-019/049--SEOAI: oai:DiVA.org:liu-162476DiVA, id: diva2:1375788
Subject / course
Media Technology
Uppsok
Technology
Supervisors
Examiners
Available from: 2019-12-06 Created: 2019-12-06 Last updated: 2019-12-06Bibliographically approved

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A Client-Server Solution for Detecting Guns in School Environment using Deep Learning Techniques(2634 kB)33 downloads
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CiteExportLink to record
Permanent link

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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
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