Multispot Image Denoising Correction and 3-D Shape Measurement of Cylindrical Shaft Parts Based on Deep Learning

Cylindrical shaft parts are critical in optical applications where performance is highly dependent on surface quality and therefore require highly refined surface characteristics. When using a multibeam angle sensor (MBAS) for cylindrical shaft parts inspection, the inspection accuracy is reduced due to subspot distortion caused by long camera exposure times and natural light interference. On this basis, a multispot image denoising and correction network (MSIDNet) is proposed, aiming at processing multispot images by denoising and correction techniques, in addition to combining Gaussian fitting methods (GFMs) to improve subspot localization accuracy to the subpixel level. A subspot offset determination method based on moving window matching subspots is proposed, which provides a reliable basis for the 3-D reconstruction of cylindrical shaft parts. Experimental results show that MSIDNet can effectively remove noise interference and achieve accurate extraction of subpixel level subspot centers. Cylindrical shaft parts with radii of 10, 20, and 22 mm were measured using this method, and the results showed that their standard deviations (stds) were 0.421, 0.207, and
$0.071~\mu $
m, respectively.