TY  - JOUR
PY  - 2023//
TI  - Real-time tunnel abnormal sound detection algorithm using convolutional neural networks
JO  - Journal of Korean Institute of Communications and Information Sciences
A1  - Lee, Juyoung
A1  - Park, Chunkyun
A1  - Kim, Hyunjoong
SP  - 150
EP  - 161
VL  - 48
IS  - 2
N2  - 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.<p />  <p>Language: ko</p>
LA  - ko
SN  - 1226-4717
UR  - http://dx.doi.org/10.7840/kics.2023.48.2.150
ID  - ref1
ER  -