Lightweight Deep Learning Based Intelligent Edge Surveillance Techniques
Yu Zhao, Yue Yin, Guan Gui
Abstract
Decentralized edge computing techniques have been attracted strongly attentions in many applications of intelligent Internet of Things (IIoT). Among these applications, intelligent edge surveillance (INES) methods 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 INES technique for a specific IIoT application. First, a depthwise separable convolutional strategy is introduced to build a lightweight deep neural network to reduce its computational cost. Second, we combine edge computing with cloud computing to reduce network traffic. Third, our proposed INES method is applied into the practical construction site for the validation of a specific IIoT application. The detection speed of the proposed INES reaches 16 frames per second in the edge device. After the joint computing of edge and cloud, the detection precision can reach as high as 89%. In addition, the operating cost at the edge device is only one-tenth of that of the centralized server. Experiment results are given to confirm the proposed INES method in terms of both computational cost and detection accuracy.