Effective Deep Learning-Based Infrared Spectral Gas Identification Method

In 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. This study introduces the DA-ResNet12 network, presenting an innovative method for detection of infrared spectral gas components. By enhancing the dilation residual module within a residual network, superior gas identification performance is achieved. Notably, DA-ResNet12 outperforms competitors in model efficiency, accuracy, and training speed, demonstrating its effectiveness in advancing infrared gas detection technology.image