Reconstruction of a three-dimensional temperature field in flames based on ES-ResNet18

Currently, the method of establishing the correspondence between the flame light field image and the temperature field by deep learning is widely used. Based on convolutional neural networks (CNNs), the reconstruction accuracy has been improved by increasing the depth of the network. However, as the depth of the network increases, it will lead to gradient explosion and network degradation. To further improve the reconstruction accuracy of the flame temperature field, this paper proposes an ES-ResNet18 model, in which SoftPool is used instead of MaxPool to preserve feature information more completely and efficient channel attention (ECA) is introduced in the residual block to reassign more weights to feature maps of critical channels. The reconstruction results of our method were compared with the CNN model and the original ResNet18 network. The results show that the average relative error and the maximum relative error of the temperature field reconstructed by the ES-ResNet18 model are 0.0203% and 0.1805%, respectively, which are reduced by one order of magnitude compared to the CNN model. Compared to the original ResNet18 network, they have decreased by 17.1% and 43.1%, respectively. Adding Gaussian noise to the flame light field images, when the standard deviation exceeds 0.03, the increase in reconstruction error of the ES-ResNet18 model is lower than that of ResNet18, demonstrating stronger anti-noise performance.