| Summary: | At present, low emissivity coating has been widely used in various fields, but damage will greatly reduce the efficiency of low emissivity coating, so the damage detection of low emissivity coating becomes an important work. Based on convolution neural network, a model for automatic identification of coating damage with low emissivity is proposed. Firstly, the optical image data set of low emissivity coating is constructed and extended by means of data enhancement. After that, VGG-19 and ResNet-50 models are built based on tensorflow, and the cross entropy loss function is used in the models. Then, SGD, momentum, RMSprop and Adam are used to optimize the model. In the process of model optimization, the learning rate is adjusted to get the optimal model. The results show that when the learning rate is $5\times 10^{-5}$ and Adam method is used to optimize the model, the recognition accuracy of VGG-19 model is 90.64%, while that of ResNet-50 model is 94.14%. This paper is of great significance for the study of automatic damage identification of low emissivity coatings.
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