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Convolutional neural network-based classification system design with compressed wireless sensor network images

With the introduction of various advanced deep learning algorithms, initiatives for image classification systems have transitioned over from traditional machine learning algorithms (e.g., SVM) to Convolutional Neural Networks (CNNs) using deep learning software tools. A prerequisite in applying CNN...

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Autores principales: Ahn, Jungmo, Park, JaeYeon, Park, Donghwan, Paek, Jeongyeup, Ko, JeongGil
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5940226/
https://www.ncbi.nlm.nih.gov/pubmed/29738564
http://dx.doi.org/10.1371/journal.pone.0196251
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author Ahn, Jungmo
Park, JaeYeon
Park, Donghwan
Paek, Jeongyeup
Ko, JeongGil
author_facet Ahn, Jungmo
Park, JaeYeon
Park, Donghwan
Paek, Jeongyeup
Ko, JeongGil
author_sort Ahn, Jungmo
collection PubMed
description With the introduction of various advanced deep learning algorithms, initiatives for image classification systems have transitioned over from traditional machine learning algorithms (e.g., SVM) to Convolutional Neural Networks (CNNs) using deep learning software tools. A prerequisite in applying CNN to real world applications is a system that collects meaningful and useful data. For such purposes, Wireless Image Sensor Networks (WISNs), that are capable of monitoring natural environment phenomena using tiny and low-power cameras on resource-limited embedded devices, can be considered as an effective means of data collection. However, with limited battery resources, sending high-resolution raw images to the backend server is a burdensome task that has direct impact on network lifetime. To address this problem, we propose an energy-efficient pre- and post- processing mechanism using image resizing and color quantization that can significantly reduce the amount of data transferred while maintaining the classification accuracy in the CNN at the backend server. We show that, if well designed, an image in its highly compressed form can be well-classified with a CNN model trained in advance using adequately compressed data. Our evaluation using a real image dataset shows that an embedded device can reduce the amount of transmitted data by ∼71% while maintaining a classification accuracy of ∼98%. Under the same conditions, this process naturally reduces energy consumption by ∼71% compared to a WISN that sends the original uncompressed images.
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spelling pubmed-59402262018-05-18 Convolutional neural network-based classification system design with compressed wireless sensor network images Ahn, Jungmo Park, JaeYeon Park, Donghwan Paek, Jeongyeup Ko, JeongGil PLoS One Research Article With the introduction of various advanced deep learning algorithms, initiatives for image classification systems have transitioned over from traditional machine learning algorithms (e.g., SVM) to Convolutional Neural Networks (CNNs) using deep learning software tools. A prerequisite in applying CNN to real world applications is a system that collects meaningful and useful data. For such purposes, Wireless Image Sensor Networks (WISNs), that are capable of monitoring natural environment phenomena using tiny and low-power cameras on resource-limited embedded devices, can be considered as an effective means of data collection. However, with limited battery resources, sending high-resolution raw images to the backend server is a burdensome task that has direct impact on network lifetime. To address this problem, we propose an energy-efficient pre- and post- processing mechanism using image resizing and color quantization that can significantly reduce the amount of data transferred while maintaining the classification accuracy in the CNN at the backend server. We show that, if well designed, an image in its highly compressed form can be well-classified with a CNN model trained in advance using adequately compressed data. Our evaluation using a real image dataset shows that an embedded device can reduce the amount of transmitted data by ∼71% while maintaining a classification accuracy of ∼98%. Under the same conditions, this process naturally reduces energy consumption by ∼71% compared to a WISN that sends the original uncompressed images. Public Library of Science 2018-05-08 /pmc/articles/PMC5940226/ /pubmed/29738564 http://dx.doi.org/10.1371/journal.pone.0196251 Text en © 2018 Ahn et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Ahn, Jungmo
Park, JaeYeon
Park, Donghwan
Paek, Jeongyeup
Ko, JeongGil
Convolutional neural network-based classification system design with compressed wireless sensor network images
title Convolutional neural network-based classification system design with compressed wireless sensor network images
title_full Convolutional neural network-based classification system design with compressed wireless sensor network images
title_fullStr Convolutional neural network-based classification system design with compressed wireless sensor network images
title_full_unstemmed Convolutional neural network-based classification system design with compressed wireless sensor network images
title_short Convolutional neural network-based classification system design with compressed wireless sensor network images
title_sort convolutional neural network-based classification system design with compressed wireless sensor network images
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5940226/
https://www.ncbi.nlm.nih.gov/pubmed/29738564
http://dx.doi.org/10.1371/journal.pone.0196251
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