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.

中文翻译：

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基于深度学习的有效红外光谱气体识别方法

为了快速准确地检测红外光谱气体成分，本研究提出了一种改进的膨胀残差模块，用膨胀卷积代替经典卷积模块，使其具有更宽的感受野。基于残差网络，通过减少卷积核的大小和膨胀卷积模块的数量，开发了一种高效且有效的膨胀残差网络DA-Resnet12，用于红外光谱气体识别。采用分类准确率、训练持续时间和模型参数大小作为评估指标。实验结果表明，所提出的 DA-ResNet12 网络在模型参数数量、准确性和时间效率方面优于其他对比方法，证明了所提出的 DA-ResNet12 网络模型的有效性和效率。