Surface roughness prediction of aircraft after coating removal based on optical image and deep learning

Abstract

Abstract To quickly evaluate the surface quality of aircraft after coating removal, a surface roughness prediction method based on optical image and deep learning model is proposed. In this paper, the "optical image-surface roughness" data set is constructed, and SSEResNet for regression prediction of surface roughness is designed by using feature fusion method. SSEResNet can effectively extract the detailed features of optical images, and Adam method is used for training optimization. Experiments show that the proposed model outperforms the other seven CNN backbone networks compared. This paper also investigates the effect of four different learning rate decay strategies on model training and prediction performance. The results show that the learning rate decay method of Cosine Annealing with warm restart has the best effect, its test MAE value is 0.245 μm, and the surface roughness prediction results are more consistent with the real value. The work of this paper is of great significance to the removal and repainting of aircraft coatings.