Abstract: A temperature correction model, EACN, based on a channel attention mechanism is proposed to address the issues of insufficient accuracy and slow speed in temperature measurements from thermal imaging cameras. First, the model parameters are reduced by decreasing the features through 1x1 convolution. Second, we introduce a channel attention mechanism, ECA, to enhance the feature saliency expression between channels in the feature mapping module stage, compensating for lost feature information during dimensionality reduction and compression, thereby further improving the feature characterization capability of the model. Finally, through skip connections, shallow feature information is combined with semantic space information in the feature reconstruction stage, thus improving temperature correction accuracy. In this experiment, two data strategies were used on a self-built dataset. The experimental results show that the EACN model outperforms the SRCNN and VDSR models in both correction accuracy and speed.