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International Conference on Sensor Systems and Software

S-CUBE 2016: Sensor Systems and Software pp 118–129Cite as

Energy Consumption and Data Amount Reduction Using Object Detection on Embedded Platform

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Abstract

High resolution image handling often results with high energy burden for battery-powered devices, such as sensor nodes in WSN. Motivation for this study is assessment of energy consumption of the sensor node with high-resolution camera, featuring image processing. We present a selection of object detection algorithms and evaluate their efficiency. To verify applicability of those algorithms, we acquired image sequence that correspond to applications of pests detection in agriculture. We verified considered algorithms’ performances: recall, precision and expected reduction of the data amount. Energy required to execute considered algorithms was measured on ARM processor based platform. Our results show that object extraction on a node can provide reduction of the data amount by up to three orders of magnitude. While simple algorithms can lead to lower overall energy consumption of the node, the more complex algorithm provides better performances, but at a cost of prohibitively high energy consumption.

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Authors and Affiliations

  1. Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia

    Boris Snajder, Zoran Kalafatic & Vedran Bilas

Authors
  1. Boris Snajder
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  2. Zoran Kalafatic
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  3. Vedran Bilas
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Corresponding author

Correspondence to Boris Snajder .

Editor information

Editors and Affiliations

  1. ITET, ETH Zurich, Zurich, Switzerland

    Michele Magno

  2. Laboratoire d’Electronique, Nice Sophia Antipolis University, Sophia Antipolis, France

    Fabien Ferrero

  3. Faculty of Electrical Engineering, University of Zagreb, Zagreb, Croatia

    Vedran Bilas

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© 2017 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Snajder, B., Kalafatic, Z., Bilas, V. (2017). Energy Consumption and Data Amount Reduction Using Object Detection on Embedded Platform. In: Magno, M., Ferrero, F., Bilas, V. (eds) Sensor Systems and Software. S-CUBE 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 205. Springer, Cham. https://doi.org/10.1007/978-3-319-61563-9_10

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  • DOI: https://doi.org/10.1007/978-3-319-61563-9_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61562-2

  • Online ISBN: 978-3-319-61563-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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