In-tunnel Accident Detection System based on the Learning of Accident Sound
Linyang Yan1, *, Sun-Woo Ko1
Identifiers and Pagination:Year: 2021
First Page: 81
Last Page: 92
Publisher Id: TOTJ-15-81
Article History:Received Date: 21/9/2020
Revision Received Date: 16/1/2021
Acceptance Date: 05/2/2021
Electronic publication date: 21/05/2021
Collection year: 2021
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.
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.
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.
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.