There exist some shallow scratch defects on the super-smooth optical surface. Their detection has a low efficiency with the existing technologies. So a new detection method, dark-field detection of adaptive smoothing and morphological differencing (DFD-ASMD), is proposed. On one hand, the information of shallow scratches can be kept in dark-field images. On the other hand, their weak characteristics can be separated and protected from being overly reduced during the elimination of noise and background in the image. Experiments show the detection rate of shallow scratches is around 82%, and DFD-ASMD can lay a foundation for quality control of defects on the high-quality optical surface.
In the field of automatic optical inspection, it is imperative to measure the defects on spherical optical surfaces. So a novel spherical surface defect evaluation system is established in this paper to evaluate defects on optical spheres. In order to ensure the microscopic scattering dark-field imaging of optical spheres with different surface shape and radius of curvature, illumination with variable aperture angle is employed. In addition, the scanning path of subapertures along the parallels and meridians is planned to detect the large optical spheres. Since analysis shows that the spherical defect information could be lost in the optical imaging, the three-dimensional correction based on a pin-hole model is proposed to recover the actual spherical defects from the captured two-dimensional images. Given the difficulty of subaperture stitching and defect feature extraction in three-dimensional (3D) space after the correction, the 3D subapertures are transformed into a plane to be spliced through geometric projection. Then, methods of the surface integral and calibration are applied to quantitatively evaluate the spherical defects. Furthermore, the 3D panorama of defect distribution on the spherical optical components can be displayed through the inverse projective reconstruction. Finally, the evaluation results are compared with the OLYMPUS microscope, testifying to the micrometer resolution, and the detection error is less than 5%.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.