Open AccessIn-tunnel Accident Detection System based on the Learning of Accident Sound
Author(s) -
Linyang Yan,
Sun-Woo Ko
Publication year - 2021
Publication title -
the open transportation journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.293
H-Index - 9
eISSN - 2667-1212
pISSN - 1874-4478
DOI - 10.2174/1874447802115010081
Subject(s) - mel frequency cepstrum , accident (philosophy) , sound (geography) , artificial neural network , computer science , cepstrum , sound analysis , speech recognition , engineering , artificial intelligence , acoustics , feature extraction , philosophy , physics , epistemology
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.