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 (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.