The inspection of particles plays an important role in quality control of smooth surfaces, such as optics and silicon wafers. The difficulty of inspection lies not only in the high-resolution detectivity for small-size particles, but also in the need to distinguish among different types of particles due to their different damages and re-work processes. This paper proposes a detection and discrimination method of the particles on (surface particles) and below smooth surfaces (subsurface particles) by laser scattering with polarization measurement. The incident laser beam passes through a polarization state generator (PSG) to illuminate the smooth surface. A large proportion of the particle-induced scattered light is detected after collected by an ellipsoidal mirror. Meanwhile, in the blind area of the ellipsoidal mirror, a small amount of scattered light enters the polarization measurement channel, where a polarization state analyzer (PSA) is used to finally obtain a two-dimensional polarization signal for discriminating the two types of particles. Then, by a nonlinear global search combined with the theoretical models for polarized light scattering from particles, the optimal polarization measurement state of the PSG and the PSA is obtained to maximize the separability between the surface particles and subsurface particles on the acquired polarization signals. This method takes full advantage of the polarization of scattered light, and with one single scan the inspection for the whole surface can be accomplished. Finally, experimental results demonstrate the effectiveness and accuracy of the method.
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.
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