RESEARCH ARTICLE


In-tunnel Accident Detection System based on the Learning of Accident Sound



Linyang Yan1, *, Sun-Woo Ko1
1 Department of Culture Technology, Graduate School, Jeonju University, Jeonju, South Korea


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Creative Commons License
© 2021 Yan and Ko.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: (https://creativecommons.org/licenses/by/4.0/legalcode). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the Department of Culture Technology, Graduate School, Jeonju University, Jeonju, South Korea;
Tel: +8210-3146-9262; E-mail: yanlinyang@naver.com


Abstract

Introduction:

Traffic accidents are easy to occur in the tunnel due to its special environment, and the consequences are very serious. The existing vehicle accident detection system and CCTV system have the issues of low detection rate.

Methods:

A method of using Mel Frequency Cepstrum Coefficient (MFCC) to extract sound features and using a deep neural network (DNN) to learn sound features is proposed to distinguish accident sound from the non-accident sound.

Results and Discussion:

The experimental results show that the method can effectively classify accident sound and non-accident sound, and the recall rate can reach more than 78% by setting appropriate neural network parameters.

Conclusion:

The method proposed in this research can be used to detect tunnel accidents and consequently, accidents can be detected in time and avoid greater disasters.

Keywords: Accident sound classification, Accident sound detection, Deep neural network, MFCC, Traffic accidents, Tunnel accidents.