High-precision autocollimation method based on a multiscale convolution neural network for angle measurement.

A high-precision autocollimation method based on multiscale convolution neural network (MSCNN) for angle measurement is proposed. MSCNN is integrated with the traditional measurement model. Using the multiscale representation learning ability of MSCNN, the relationship between spot shape (large-scale feature), gray distribution (small-scale feature), and the influence of aberration and assembly error in the collimating optical path is extracted.