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Image Scaling Effects on Deep Learning Based Applications
Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.
Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.
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2022 (English)In: 2022 IEEE International Symposium on Measurements & Networking (M&N), IEEE, 2022Conference paper, Published paper (Refereed)
Abstract [en]

The sophistication and high accuracy of Deep Neural Networks have gotten significant attention in recent years, with a wide range of applications making use of their capabilities. However, the deployment of such networks still faces limitations due to the high volume of data to be processed and the high computational requirements. In this article we focus on the effects that data volume reduction, due to image compression and scaling down the image resolution, will have on the detection accuracy for the design case of a powered wheelchair guidance system. Throughout our analysis we show that the reduction in image resolution to a factor of 16× in image area alongside with JPEG compression provides a detection accuracy of over 0.93 in mAP, while the additional error in the position estimation of the caregiver is less than 0.5 cm. By reducing the data volume we inherently reduce the communication energy consumption, which is reduced by more than one order of magnitude. These results prove that we can overcome the complexity of high data volume for the deployment of DNNs in resource constrained IoT applications by interlacing the effects of image compression and resolution reduction, maintaining the accuracy and reducing the node energy consumption.

Place, publisher, year, edition, pages
IEEE, 2022.
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:miun:diva-46175DOI: 10.1109/MN55117.2022.9887705ISI: 000885015100020Scopus ID: 2-s2.0-85140913801ISBN: 978-1-6654-8362-9 (electronic)OAI: oai:DiVA.org:miun-46175DiVA, id: diva2:1700015
Conference
2022 IEEE International Symposium on Measurements & Networking (M&N)
Available from: 2022-09-29 Created: 2022-09-29 Last updated: 2022-12-01Bibliographically approved

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Shallari, IridaO'Nils, MattiasHussain, Mazhar

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CiteExportLink to record
Permanent link

Direct link
Cite
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