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학술지 Convolutional Neural Network-based Classification System Design with Compressed Wireless Sensor Network Images
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안정모, 박재연, 박동환, 백정엽, 고정길
PLOS ONE, v.13 no.5, pp.1-25
Public Library of Science
18HH2900, 사물 스스로 학습이 가능한 IoT 공통 SW 엔진 개발, 박동환
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 제안 키워드
CNN model, Classification system, Color Quantization, Compressed data, Convolution neural network(CNN), Data Collection, Embedded Devices, High-resolution, Image Resizing, Image classification, Image datasets