Application of Natural Gas Pipeline Leakage Detection Based on Improved DRSN-CW

Application of Natural Gas Pipeline Leakage Detection Based on Improved DRSN-CW

Natural gas

Natural gas pipelines

Convolution

Gases

Fiber optic sensors

Large dataset

Computer vision

Aiming at solving the natural gas leakage detection issue, we propose an improved method based on deep residual network with channel-wise thresholds (DRSN-CW) to improve the detection accuracy with GPLA-12 dataset. In the approach, larger and unequal convolution kernel size are designed in all convolution layers to extend the receptive field in the process of extracting fault feature. Moreover, considering that datasets of natural gas pipeline leakage typically contain large amounts of ambient noise, the soft threshold module of DRSN-CW is combined with designed kernel size to reduce the influence of noise on accuracy of gas pipeline leakage detection. Compared with the-state-of-art techniques (e.g., CNN, DRSN-CW and DRSN-CS), experimental results show that our method outperforms the compared methods. 2021 IEEE.

514-518

2021 IEEE International Conference on Emergency Science and Information Technology, ICESIT 2021, November 22, 2021 - November 24, 2021

2021

Liao, Hongcheng

Zhu, Wenwen

Zhang, Benzhu

Zhang, Xiang

Sun, Yu

Wang, Cending

Li, Jie

conferencePaper

10.1109/ICESIT53460.2021.9696455