Dy-GNet: a lightweight and efficient 1DCNN-based network for leakage aperture identification

Abstract

The deep neural network’s gas leakage aperture recognition method has high accuracy, but its low computational efficiency due to its complex structure greatly limit its application in resource-limited industrial environments and real-time processing. In this paper, we proposed an efficient recognition method based on 1D convolutional neural network. First, wavelet scattering coefficients with time-frequency information are obtained using the wavelet scattering transform. Second, the dynamic convolution is used to deepen the feature extraction and partial convolution to speed up the inference time, and the efficient Dy-G module is constructed. Finally, the Dy-G module is stacked to construct Dy-GNet to achieve the classification task. The performance of the model is verified in a noisy environment, and the results show that the model floating-point operations are 17.24 M and 83.94% accuracy is achieved at signal to noise ratio = 0 dB, which guarantees the accuracy while speeding up the inference speed with high efficiency.