Distortion Correction Method of Interference Projection Based on Convolutional Neural Network

In the aspherical surface zero position interference detection, there is a projection distortion between the measurement error distribution and the actual error distribution of the surface to be measured. Aiming at the problems of complex calculation and poor generality of current projection distortion correction methods, a correction method based on a convolutional neural network (CNN) is proposed. In this method, an intersecting parallels flexible occlude is added to the surface, and the interference image is synthesized according to the range of projected distortion coefficient as the data set of CNN. Then the appropriate network structure to train the network based on the data set is selected. Finally, the actual interference image is input into the network to predict the distortion coefficient, and to realize the calibration and correction of the projection distortion. Experimental results show that the theoretical correction error of this method is less than 1 pixel, and the actual error correction accuracy is better than that of the traditional marker method, which proves that the method is efficient and feasible.