Effective Deep Learning‐Based Infrared Spectral Gas Identification Method

Abstract

AbstractIn order to detect infrared spectral gas components fast and correctly, an improved dilation residual module is proposed in this study by substituting the classic convolution module with the dilation convolution to have a broad receptive field. Based on the residual network, an efficient and effective dilation residual network called DA‐Resnet12 is developed for infrared spectral gas identification by reducing the size of the convolution kernel and the number of dilation convolution modules. The classification accuracy, training duration, and model parametric size are employed as assessment indices. The experimental results reveal that the proposed DA‐ResNet12 network outperforms other comparative methods in terms of model parameter number, accuracy, and time efficiency, proving the efficacy and efficiency of the proposed DA‐ResNet12 network model.