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A Real-time Border Surveillance System using Deep Learning and Edge Computing by Dang-Khoa Luong-Huu; Tan-An Ngo; Huy-Tan Thai; Kim-Hung Le is a scholarly article available to read on EtoBox.

What is A Real-time Border Surveillance System using Deep Learning and Edge Computing about?

Border security has always been one of the top priority obligations and responsibilities in protecting the peace of nations. Most nations have thousands of kilometers of long borders, where illegal activities occur frequently. To ensure safety, robust and timely border surveillance systems are in high demand. However, current border surveillance systems using cloud architecture have faced several problems with high latency, bandwidth consumption, and security risks. In this paper, we introduce a realtime border surveillance system based on edge computing that shifts computation tasks from cloud to edge, resulting in alleviating existing problems. In detail, we produce a lightweight human detection model based on the MobileNet architecture, namely BorderEdge, to operate on resourceconstrained devices effectively. Our experiment results on Raspberry Pi 4 show that our system could achieve high accuracy with 29.6 frames per second (FPS) and a low memory footprint.

Author

Dang-Khoa Luong-Huu; Tan-An Ngo; Huy-Tan Thai; Kim-Hung Le

Publisher

IEEE

Published

2022

Language

EN