A federated learning-based license plate recognition scheme for 5G-enabled Internet of vehicles

- Kong, Xiangjie, Wang, Kailai, Hou, Mingliang, Hao, Xinyu, Shen, Guojiang, Chen, Xin, Xia, Feng

Title
A federated learning-based license plate recognition scheme for 5G-enabled Internet of vehicles
Creator
Kong, Xiangjie; Wang, Kailai; Hou, Mingliang; Hao, Xinyu; Shen, Guojiang; Chen, Xin; Xia, Feng
Date
2021
Type
Text; Journal article
Identifier
http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/185792
Identifier
vital:16750
Identifier
https://doi.org/10.1109/tii.2021.3067324
Identifier
ISBN:1941-0050
Abstract
License plate is an essential characteristic to identify vehicles for the traffic management, and thus, license plate recognition is important for Internet of Vehicles. Since 5G has been widely covered, mobile devices are utilized to assist the traffic management, which is a significant part of Industry 4.0. However, there have always been privacy risks due to centralized training of models. Also, the trained model cannot be directly deployed on the mobile device due to its large number of parameters. In this article, we propose a federated learning-based license plate recognition framework (FedLPR) to solve these problems. We design detection and recognition model to apply in the mobile device. In terms of user privacy, data in individuals is harnessed on their mobile devices instead of the server to train models based on federated learning. Extensive experiments demonstrate that FedLPR has high accuracy and acceptable communication cost while preserving user privacy.
Relation
IEEE Transactions on Industrial Informatics Vol. 17, no. 12 (Dec 2021), p. 8523-8530
Rights
All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
Rights
Copyright © 2021 IEEE
Rights
Open Access
Subject
40 Engineering; 46 Information and computing sciences
Full Text
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