Aiming at a series of problems in reverse osmosis film coating process

such as defect detection still depends on manual completion

high recognition difficulty

low detection efficiency and non-intelligence

a method of coating defect identification using deep convolution neural network is designed on the basis of typical neural network structure

and a reasonable volume is designed. Based on the hierarchical structure and parameters of product neural network

an appropriate neural network recognition model is constructed according to the characteristics of a large number of original defect images. The experimental results show that the correct recognition rate of coating defect detection algorithm based on deep convolution neural network is more than 92 %

and it can detect defects quickly and accurately. The detection and recognition technology can be applied to similar coating manufacturing fields

which has important theoretical research value and practical significance.