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Lightweight Deep Learning based Intelligent Edge Surveillance Techniques

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posted on 19.02.2020 by Yu Zhao, Yue Yin, Guan Gui
Decentralized edge computing techniques have been attracted strongly attentions in many applications of intelligent internet of things (IIoT). Among these applications, intelligent edge surveillance (LEDS) techniques play a very important role to recognize object feature information automatically from surveillance video by virtue of edge computing together with image processing and computer vision. Traditional centralized surveillance techniques recognize objects at the cost of high latency, high cost and also require high occupied storage. In this paper, we propose a deep learning-based LEDS technique for a specific IIoT application. First, we introduce depthwise separable convolutional to build a lightweight neural network to reduce its computational cost. Second, we combine edge computing with cloud computing to reduce network traffic. Third, we apply the proposed LEDS technique into the practical construction site for the validation of a specific IIoT application. The detection speed of our proposed lightweight neural network reaches 16 frames per second in edge devices. After cloud server fine detection, the precision of the detection reaches 89\%. In addition, the operating cost at the edge device is only one-tenth of that of the centralized server.

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Email Address of Submitting Author

guiguan@njupt.edu.cn

Submitting Author's Institution

Nanjing University of Posts and Telecommunications

Submitting Author's Country

China

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in IEEE Transactions on Cognitive Communications and Networking

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