Real-Time Tunnel Abnormal Sound Detection Algorithm Using Convolutional Neural Networks 


Vol. 48,  No. 2, pp. 150-161, Feb.  2023
10.7840/kics.2023.48.2.150


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  Abstract

In the traffic industry, the automatic accident detection system is a major concern. Although image-based and radar-based traffic accident detection systems are commonly employed, they have several drawbacks, including the need to secure the camera's field of view, a high rate of false alarms, and a lengthy detection time. Using a real-time acoustic surveillance system and the classification algorithm via Convolutional Neural Network (CNN), this article proposes several methods for identifying abnormal situations, such as a car crash or tire skid sound, to overcome the limitations of existing methods. We create an audio database by collecting sounds from two tunnels in South Korea using self-made microphones for eight months and classifying them into three categories: car crash, tire skid, and normal environmental sounds. We establish a three-step classification procedure using an algorithm. We compare the detection rate and false alarm rate of our proposed method to those of deep learning techniques including MLP (Multi-Layer Perceptron), Long-Short Term Memory, ShuffleNetv2, and MobileNetv2. In addition, we present a method for filtering out irrelevant sound data to improve the computational efficiency of our approach.

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[IEEE Style]

J. Lee, C. Park, H. Kim, "Real-Time Tunnel Abnormal Sound Detection Algorithm Using Convolutional Neural Networks," The Journal of Korean Institute of Communications and Information Sciences, vol. 48, no. 2, pp. 150-161, 2023. DOI: 10.7840/kics.2023.48.2.150.

[ACM Style]

Juyoung Lee, Chunkyun Park, and Hyunjoong Kim. 2023. Real-Time Tunnel Abnormal Sound Detection Algorithm Using Convolutional Neural Networks. The Journal of Korean Institute of Communications and Information Sciences, 48, 2, (2023), 150-161. DOI: 10.7840/kics.2023.48.2.150.

[KICS Style]

Juyoung Lee, Chunkyun Park, Hyunjoong Kim, "Real-Time Tunnel Abnormal Sound Detection Algorithm Using Convolutional Neural Networks," The Journal of Korean Institute of Communications and Information Sciences, vol. 48, no. 2, pp. 150-161, 2. 2023. (https://doi.org/10.7840/kics.2023.48.2.150)
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pISSN : 1226 - 4717
eISSN : 2287 - 3880