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Road surface real-time detection based on Raspberry Pi and recurrent neural networks

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journal contribution
posted on 19.04.2021, 12:46 by Junyi Wang, Qinggang MengQinggang Meng, Peng Shang, Mohamad SaadaMohamad Saada
This paper focuses on road surface real-time detection by using tripod dolly equipped with Raspberry Pi 3 B+, MPU 9250, which is convenient to collect road surface data and realize real-time road surface detection. Firstly, six kinds of road surfaces data are collected by utilizing Raspberry Pi 3 B+ and MPU 9250. Secondly, the classifiers can be obtained by adopting several machine learning algorithms, recurrent neural networks (RNN) and long short-term memory (LSTM) neural networks. Among the machine learning classifiers, gradient boosting decision tree has the highest accuracy rate of 97.92%, which improves by 29.52% compared with KNN with the lowest accuracy rate of 75.60%. The accuracy rate of LSTM neural networks is 95.31%, which improves by 2.79% compared with RNN with the accuracy rate of 92.52%. Finally, the classifiers are embedded into the Raspberry Pi to detect the road surface in real time, and the detection time is about one second. This road surface detection system could be used in wheeled robot-car and guiding the robot-car to move smoothly.

Funding

National Natural Science Foundation of China under Grant 61903075

YOBAN Project under Newton Fund/Innovate UK 102871

Intelligent Robot for Assisting Old People Project under 2016YFE0124100

Natural Science Foundation of Liaoning Province under Grant 2019-KF-03-02

Fundamental Research Funds for the Central Universities under Grant N2026003

Chunhui Plan Cooperative Project of Ministry of Education under Grant LN2019006

History

School

  • Science

Department

  • Computer Science

Published in

Transactions of the Institute of Measurement and Control

Volume

43

Issue

11

Pages

2540-2550

Publisher

SAGE Publications

Version

AM (Accepted Manuscript)

Rights holder

© The authors

Publisher statement

This paper was accepted for publication in the journal Transactions of the Institute of Measurement and Control and the definitive published version is available at https://doi.org/10.1177/01423312211003372. Users who receive access to an article through a repository are reminded that the article is protected by copyright and reuse is restricted to non-commercial and no derivative uses. Users may also download and save a local copy of an article accessed in an institutional repository for the user's personal reference. For permission to reuse an article, please follow our Process for Requesting Permission

Publication date

2021-04-11

Copyright date

2021

ISSN

0142-3312

eISSN

1477-0369

Language

en

Depositor

Prof Qinggang Meng. Deposit date: 16 April 2021