In view of the difficulty of defects detection of complex metal curve surface in uneven illumination and high speed processing, a new, simple, yet robust algorithm based on statistical feature of local visual field is proposed. This algorithm first performs the ideal image difference by extracting the template from the image itself, and then computes the statistical feature in local visual field to correct the gray-scale fluctuation in each region of image. In this way, the influence of the uneven illumination at low and high frequency is eliminated concurrently, which achieves the equalization of the statistical features of the local visual fields except the position containing the defect, so as to use the global threshold in whole image reasonably; Next, on the search of defects, this paper replaces the pixel level with the local field of vision and compresses the image information with the defects’ scale which is in line with the human eye. This not only reduces the influence of random noise, but also greatly improves the processing speed while preserving defects information, which makes it possible to realize real-time processing ability for image with the large amount of data. To detect complex curved surface on semi-finished metal shell of cell phone, the experimental results demonstrate that the defects detection accuracy of the proposed algorithm can reach 95%, and the detection time for single test area is less than 1ms, which is suitable for accurate and real-time detection on the production line for such surface defect.
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.
In the inertial confinement fusion system (ICF), surface scratches of the large diameter optical surface appear as dot lines (punctate scratches). This kind of scratches is only detected under a high microscope magnification system. This can be caused by the blemishes on the optical processing technology and shallow scratches (< 25nm ). As a result, it can have an impact on the relevant calculation of the width and length of the scratches. Besides, this kind of scratches has a serious impact on the ICF, such as system damage. To solve this problem, this paper proposes the image pattern charter of punctate scratches based on the existing surface defects detection system (SDES). Finally, it proposes an algorithm of scratches based on the linearity differential detection and connectivity. That is, using coordinate transformation and direction differential-threshold discrimination, the scratches can be connected effectively and calculated exactly. Experimental results show that punctate scratches parts can be connected correctly, and the accuracy of the calculated length reaches 95%. Also, the improved algorithm applies to the arc-shaped scratches, which is based the block image processing. Currently, this algorithm can be applied to connect and calculate the shallow scratches accurately and precisely on large fine optics in the ICF system. Thus it can also decrease the misdetection rate of nonconforming super-smooth optics in the ICF system.
The high-resolution detecting system based on machine vision for defects on large aperture and super-smooth surface uses a novel ring telecentric lighting optical system detecting the defects on the sample all round and without blind spots. The scattering light induced by surface defects enters the adaptive and highly zoom microscopic scattering dark-field imaging system for defect detecting and then forms digital images. Sub-aperture microscopic scanning sampling and fast stitching on the surface is realized by using precise multi-axis shifting guided scanning system and a standard comparison board based upon binary optics is used to implement fast calibration of micron-dimension defects detected actually. The pattern recognition technology of digital image processing which can automatically output digitalized surface defects statements after scaling is established to comprehensively evaluate defects. This system which can reach micron-dimension defect resolution can achieve detections of large aperture components of 850 mm × 500 mm, solve the durable problem of subjective uncertainty brought in by human visual detection of defects and achieve quantitative detection of defects with machine vision.
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