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학술대회 Deep-Learning-Based Pipe Leak Detection Using Image-Based Leak Features
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저자
배지훈, 여도엽, 윤두병, 오세원, 김관중, 김내수, 표철식
발행일
201810
출처
International Conference on Image Processing (ICIP) 2018, pp.2361-2365
DOI
https://dx.doi.org/10.1109/ICIP.2018.8451489
초록
In this paper, we propose a deep-learning-based pipe leak detection (PLD) technique using trajectory-based image features extracted from time-series acoustic data received from microphone sensor nodes. We developed root-mean-square-pattern and frequency-pattern images by reflecting the leakage signal characteristics in the time and frequency domains and used them for ensemble learning with the help of state-of-the-art residual networks. The experimental results obtained using the measured data of leakage signals in a laboratory-scale nuclear power plant environment are presented to validate the effectiveness of the proposed method for PLD. The results show that the proposed image features suitable for convolutional neural network-based deep-learning can provide reliable PLD performance in terms of classification accuracy despite the machine-driven complex noise environment.
키워드
Acoustic signal, Deep-learning, Image feature, Leak detection, Residual network
KSP 제안 키워드
Acoustic data, Acoustic signal, Convolution neural network(CNN), Image feature, Image-based, Laboratory scale, Learning-based, Nuclear Power Plant(NPP), Residual Network, Root mean square(RMS), Sensor node