Aimed at the problem of strong background interference introduced in digital image processing from complex surfaces under industrial defect detection, a method for complex surface defect detection based on human visual characteristics and feature extracting is proposed. Inspired by the visual attention mechanism, defect areas can be identified from the background noise conveniently by human eyes. We introduce the improved grayscale adjustment and frequency-tuned saliency algorithm combined with the salient region mask obtained by dilation and differential operation to eliminate the background noise and extract defect areas. Meanwhile the directional feature matching and merging algorithm is applied to enhance directional features and retain details of defects. Testing images are captured by our established detecting system. Experimental results show that our method can retain defect information completely and achieve considerable extracting efficiency and detecting accuracy.
In inertial confinement fusion system, the intermittent scratches on the polished surface of single-sided polished and bottom surface frosted optical components are complex, and it’s of great difficulty to extract them completely. In order to solve this problem, established in the light-field surface detection system, this paper brings forward a novel intermittent scratch detection method based on adaptive sector scanning algorithm (ASC) cascading mean variance threshold algorithm (MVTH). In the preprocessing step, dividing the original image into subimages with a number of integer multiple of cpu cores so as to fully compress image processing time utilizing parallel processing, using mean filter to balance background and then obtaining binary subimages utilizing morphology and threshold operations, finally, utilizing Two-pass algorithm to label the connected domains of binary subimages. In the detection step, considering the complexity of the pattern of intermittent scratches, ASC is first used for routine intermittent scratches stitching and then supplemented by MVTH. In the verification step, in order to prove that the detected intermittent scratches satisfy the criteria for scratches in human eyes, the method of support vector machine (SVM) pattern recognition is utilized to compare the detected results with the continuous scratch samples detected by human eyes. This algorithm has high degree of parallelism, high speed and strong robustness. The experimental results illustrate that the complete extraction rate of intermittent scratches is 93.59% , the average processing time of single image is merely 0.029 second and the accuracy rate of detection is up to 98.72% by SVM verification.
Surface defects evaluation system (SDES) works on the principle of microscopic scattering dark-field imaging (MS-DFI) and takes the criterion board as reference for calibration. Unfortunately, for criterion board with rectangular section scribed lines, image width of narrow lines doesn’t follow the linear law, making it confusing to get real width. Besides, other criterion board except with rectangular section scribed lines in a flat plane is difficult to fabricate, which limits measurement accuracy and extensive use of SDES. In this paper, a 3D simulation model is established to simulate scatter light distribution induced by surface defects. The interactions between the incident light and surface defects in near field is calculated with the help of Finite-Difference Time-Domain (FDTD) method, a kind of Maxwell’s solver. Skills as rotation and incoherent summation are applied to obtain results under illumination of unpolarized, broad-spectrum natural light sources in uniform annular layout. Finally, near to far field projections based on vector diffraction theory is carried out to get scatter light intensity distribution in far field. The data is also post-processed by scripts to describe imaging process simplified by a lens system so that it can be compared to experiment images. The 3D simulation model reveals MS-DFI process theoretically and may help to interpret image width of surface defects. The establishment of the 3D imaging model is an attempt to overcome the limits of the criterion board and is expected to provide reference for calibration for wider applications of SDES.
This paper introduces a spherical optical surface defects evaluation system (SSDES) based on the dark-field microscopic scattering imaging (DFMSI) method. The specially designed annular illuminant with variable aperture angles ensures the condition of DFMSI for spherical optical components with variable surface shapes and radii of curvature. On account of the small imaging field of view (FOV) of the SSDES relative to the large spherical optical component under test, the scanning path for subaperture images is planned along longitudes and latitudes of the spherical surface to detect the whole surface. Besides, for avoiding the misplaced subaperture images stitching due to the decenter error, a centering system is utilized to perform the alignment of the optical axis of the spherical optics in relation to the reference axis before capturing subaperture images. Then we propose a defect evaluation method, primarily involving the threedimensional (3D) image reconstruction and global coordinate transformation, the projective stitching of 3D subaperture images, and the quantitative evaluation of defects, to process the captured spherical subaperture images. Experiments results are shown in good accordance with the OLYMPUS microscope for the relative error within 5%, and validate the SSDES to the micrometer resolution.
The inspection of surface defects is one of significant sections of optical surface quality evaluation. Based on microscopic scattering dark-field imaging, sub-aperture scanning and stitching, the Surface Defects Evaluating System (SDES) can acquire full-aperture image of defects on optical elements surface and then extract geometric size and position information of defects with image processing such as feature recognization. However, optical distortion existing in the SDES badly affects the inspection precision of surface defects. In this paper, a distortion correction algorithm based on standard lattice pattern is proposed. Feature extraction, polynomial fitting and bilinear interpolation techniques in combination with adjacent sub-aperture stitching are employed to correct the optical distortion of the SDES automatically in high accuracy. Subsequently, in order to digitally evaluate surface defects with American standard by using American military standards MIL-PRF-13830B to judge the surface defects information obtained from the SDES, an American standard-based digital evaluation algorithm is proposed, which mainly includes a judgment method of surface defects concentration. The judgment method establishes weight region for each defect and adopts the method of overlap of weight region to calculate defects concentration. This algorithm takes full advantage of convenience of matrix operations and has merits of low complexity and fast in running, which makes itself suitable very well for highefficiency inspection of surface defects. Finally, various experiments are conducted and the correctness of these algorithms are verified. At present, these algorithms have been used in SDES.
The principle of microscopic scattering dark-field imaging is adopted in surface defects evaluation system (SDES) for large fine optics. However, since defects are of micron or submicron scale, scattering imaging cannot be described simply by geometrical imaging. In this paper, the simulation model of the electromagnetic field in defect scattering imaging is established on the basis of Finite-Difference Time-Domain (FDTD) method to study the scattering imaging properties of rectangular and triangular defects with different sizes by simulation. The criterion board with scribed lines and dots on it is used to carry out experiments scattering imaging and obtain grayscale value distributions of scattering dark-field images of scribed lines. The experiment results are in good agreement with the simulation results. Based on the above analysis, defect width extraction width is preliminary discussed. Findings in this paper could provide theoretical references for defect calibration in optical fabrication and inspection.
Subaperture stitching method is used for optics surface defects detection by defects imaging. Stitching based on position is efficient while stitching error induced by the error of the scanning mechanism may cause defects dislocation. According to the stitching error analysis of spherical optics defects, a method based on Monte Carlo simulation is proposed in this paper. Firs t the volumetric error model is established based on mult ibody system theory. On this basis, the stitching error model is established and applied to compute error by Monte Carlo simulation. Analyze error and then define the tolerance of the scanning mechanism to limit stitching error. Simulation results of an optical element whose diameter is 60mm show that the scanning mechanism should satisfy that the positioning accuracy and straightness in Y direction of X axis, the run-out errors in X and Y direction of B axis, and the verticality between X and Y axis are less than 1μm, the run-out errors in X and Y direction of C axis are less than 2.8μ m, the run-out errors of B and C are less than 4.6μm. Under such conditions, the stitching error will be less than 10μm.
For the Spherical Surface Defects Evaluation System (SSDES), lens centering is essential to obtain the precise scanning trace and defect features without mismatch. Based on a combination of auto-collimating microscopy and Computer-Aided Alignment (CAA), an auto-centering system that can measure the deviation of large spherical center with respect to a reference rotation axis rapidly and accurately is established in this paper. The auto-centering system allows the closedloop feedback control of spherical center according to the different images of the crosshair reticle on CCD. Image entropy algorithm is employed to evaluate image clarity determined by the auto-focus experiment of 50μm step-length. Subsequently, an improved algorithm that can search the crosshair center automatically is proposed to make the trajectory of crosshair images and the position of rotation axis more reliable based on original circle fitting algorithm by the least square method (LSM). The comparison results indicates to show the high accuracy and efficiency of the proposed fitting method with LSM.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.