Abstract
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.
Highlights
Low emissivity materials can be divided into coating materials and structural materials in terms of forming process and bearing capacity [1]
For the low emissivity coating damage data set constructed in this paper, vgg-19 network and resnet-50 network are built respectively for training, and the optimal model is solved by adjusting the optimization method and learning rate
According to the experimental results, resnet-50 network constructed in this paper is better for automatic damage identification of low emissivity coatings
Summary
Low emissivity materials can be divided into coating materials and structural materials in terms of forming process and bearing capacity [1]. Among them, coating material refers to the coating with stealth function on the surface of the structure, while structural material refers to the material with both stealth and load-bearing functions (common laminated plate and sandwich type) [2]. The research focus of low emissivity materials is coating materials [3]. This paper will study the automatic damage identification of low emissivity coating. Low emissivity coating is widely used in aircraft, ships, missiles, military vehicles and other weapons. During the life of weapon equipment, any low emissivity coating will be affected and acted by environmental factors in the process of storage, transportation and use, resulting in the changes of physical and chemical properties such as discoloration, pulverization, delamination, cracking, adhesion reduction
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