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Real time object detection on a Raspberry Pi
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
2019 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
Objektdetektering i realtid på en Raspberry Pi (Swedish)
Abstract [en]

With the recent advancement of deep learning, the performance of object detection techniques has greatly increased in both speed and accuracy. This has made it possible to run highly accurate object detection with real time speed on modern desktop computer systems. Recently, there has been a growing interest in developing smaller and faster deep neural network architectures suited for embedded devices. This thesis explores the suitability of running object detection on the Raspberry Pi 3, a popular embedded computer board. Two controlled experiments are conducted where two state of the art object detection models SSD and YOLO are tested in how they perform in accuracy and speed. The results show that the SSD model slightly outperforms YOLO in both speed and accuracy, but with the low processing power that the current generation of Raspberry Pi has to offer, none of the two performs well enough to be viable in applications where high speed is necessary.

Place, publisher, year, edition, pages
2019. , p. 20
Keywords [en]
computer vision, object detection, Raspberry Pi
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:lnu:diva-89573OAI: oai:DiVA.org:lnu-89573DiVA, id: diva2:1361039
Subject / course
Computer Science
Supervisors
Examiners
Available from: 2019-10-23 Created: 2019-10-15 Last updated: 2019-10-23Bibliographically approved

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fulltext(576 kB)298 downloads
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Type fulltextMimetype application/pdf

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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  • vancouver
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  • de-DE
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  • en-US
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  • Other locale
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Output format
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