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Journal Article Convolutional Neural Network-based Classification System Design with Compressed Wireless Sensor Network Images
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Authors
Jungmo Ahn, JaeYeon Park, Donghwan Park, Jeongyeup Paek, JeongGil Ko
Issue Date
2018-05
Citation
PLOS ONE, v.13, no.5, pp.1-25
ISSN
1932-6203
Publisher
Public Library of Science
Language
English
Type
Journal Article
DOI
https://dx.doi.org/10.1371/journal.pone.0196251
Abstract
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.
KSP Keywords
CNN model, Classification system, Color Quantization, Compressed data, Convolution neural network(CNN), Data Collection, High-resolution, Image Resizing, Image classification, Image datasets, Learning Software
This work is distributed under the term of Creative Commons License (CCL)
(CC BY)
CC BY