Distortion spot correction and center location base on deep neural network and MBAS in measuring large curvature aspheric optical element

Abstract

Large curvature aspheric optical elements are widely used in visual system. But its morphological detection is very difficult because its accuracy requirement is very high. When we use the self-developed multi-beam angle sensor (MBAS) to detect large curvature aspheric optical elements, the accuracy will be reduced due to spot distortion. Therefore, we propose a scheme combining distorted spot correction neural network (DSCNet) and gaussian fitting method to improve the detection accuracy of distorted spot center. We develop a spot discrimination method to determine spot region in multi-spot images. The spot discrimination threshold is obtained by the quantitative distribution of pixels in the connected domain. We design a DSCNet, which corrects the distorted spot to Gaussian spot, to extract the central information of distorted spot images by multiple pooling. The experimental results demonstrate that the DSCNet can effectively correct the distorted spot, and the spot center can be extracted to sub-pixel level, which improves the measurement accuracy of the MBAS. The standard deviations of plano-convex lenses with curvature radii of 500 mm, 700 mm and 1000 mm measured with the proposed method are respectively 0.0112 um, 0.0086 um and 0.0074 um.