Mixed gas concentration inversion based on the hierarchical feature fusion convolutional neural network

Recently, the detection of gas mixtures has attracted considerable attention due to its promising application in disease diagnosis, environmental monitoring and so on. However, the detection of gas mixtures is inevitably subject to the cross-interference between different components. Herein, we propose a hierarchical feature fusion convolutional neural network (CNN) model for mixed gas concentration inversion. In our experiment, mixtures of SO2, NO2 and NH3 were analyzed. SO2 and NO2 were the detected gases while NH3 was the interfering gas. The proposed model can be applied to low-resolution spectra. Compared with the model without hierarchical structure and feature fusion, the mean absolute errors of SO2 and NO2 decreased by 80.3% and 87.0% in the simulated spectral samples. The experimental results also demonstrate that the proposed model could effectively reduce the cross-interference and improve the accuracy of gas concentration inversion. This model would have a promising application in the field of gas detection and low-resolution spectra.