Wei-Chen Li's research while affiliated with Yuan Ze University and other places

Solar cells that convert sunlight into electrical energy are the main component of a solar power system. Quality inspection of solar cells ensures high energy conversion efficiency of the product. The surface of a multi-crystal solar wafer shows multiple crystal grains of random shapes and sizes. It creates an inhomogeneous texture in the surface, and makes the defect inspection task extremely difficult. This paper proposes an automatic defect detection scheme based on Haar-like feature extraction and a new clustering technique. Only defect-free images are used as training samples. In the training process, a binary-tree clustering method is proposed to partition defect-free samples that involve tens of groups. A uniformity measure based on principal component analysis is evaluated for each cluster. In each partition level, the current cluster with the worst uniformity of inter-sample distances is separated into two new clusters using the Fuzzy C-means. In the inspection process, the distance from a test data point to each individual cluster centroid is computed to measure the evidence of a defect. Experimental results have shown that the proposed method is effective and efficient to detect various defects in solar cells. It has shown a very good detection rate, and the computation time is only 0.1 s for a 550 × 550 image.

Solar power is an attractive alternative source of electricity nowadays. Solar cells, which form the basis of a solar power system, are mainly based on crystalline silicon. Many defects cannot be visually observed with the conventional CCD imaging system. This paper presents defect inspection of multi-crystalline solar cells in electroluminescence (EL) images. A solar cell charged with electrical current emits infrared light. The intrinsic crystal grain boundaries and extrinsic defects of small cracks, breaks, and finger interruptions hardly reflect the infrared light. The EL image can thus distinctly highlight barely visible defects as dark objects. However, it also shows random dark regions in the background, which makes automatic inspection in EL images very difficult. A self-reference scheme based on the Fourier image reconstruction technique is proposed for defect detection of solar cells in EL images. The target defects appear as line- or bar-shaped objects in the EL image. The Fourier image reconstruction process is applied to remove the possible defects by setting the frequency components associated with the line- and bar-shaped defects to zero and then back-transforming the spectral image into a spatial image. The defect region can then be easily identified by evaluating the gray-level differences between the original image and its reconstructed image. The reference image is generated from the inspection image itself and, thus, can accommodate random inhomogeneous backgrounds. Experimental results on a set of various solar cells have shown that the proposed method performs effectively for detecting small cracks, breaks, and finger interruptions. The computation time of the proposed method is also fast, making it suitable for practical implementation. It takes only 0.29 s to inspect a whole solar cell image with a size of 550 × 550 pixels.

In this paper, we propose a fast regularity measure for defect detection in non-textured and homogeneously textured surfaces, with specific emphasis on ill-defined subtle defects. A small neighborhood window of proper size is first chosen and they slide over the entire inspection image in a pixel-by-pixel basis. The regularity measure for each image patch enclosed in the window is then derived from the eigenvalues of the covariance matrix formed by the variance–covariance of the x- and y-coordinates with the pixel gray levels as the weights for all pixel points in the window. The two eigenvalues of the weighted covariance matrix will be approximately the same when the image patch contains only a homogeneous region, whereas the two eigenvalues will be relatively different if the image patch in the window contains a defect. The smaller eigenvalue of the covariance matrix is then used as the regularity measure. The integral image technique is introduced to the computation of the regularity measure so that it is invariant to the neighborhood window size. The proposed method uses only one single discrimination feature for defect detection. It avoids the use of complicated classifiers in a high-dimensional feature space, and requires no learning process from a set of defective and defect-free training samples. Experimental results on a variety of material surfaces found in industry, including textured images of plastic surfaces and leather and non-textured images of backside solar wafers and LCD backlight panels, have shown the effectiveness of the proposed regularity measure for surface defect detection. It is computationally very fast, and takes only 0.032 s for a 400 × 400 image on a Pentium 3.00 GHz personal computer. In a test set of 73 backside solar wafer images involving 53 defect-free and 20 defective samples, the proposed regularity measure can correctly identify all the test images.

Solar power is an attractive alternative source of electricity. Solar cells, which form the basis of a solar power system, are mainly based on crystalline silicon. Many defects cannot be visually observed with the conventional CCD imaging system. This paper presents defect inspection of multicrystalline solar cells in electroluminescence (EL) images. A solar cell charged with electrical current emits infrared light, whose intensity is lower at intrinsic crystal grain boundaries and extrinsic defects of small cracks, breaks, and finger interruptions. The EL image can distinctly highlight barely visible defects as dark objects, but it also shows random dark regions in the background, which makes automatic inspection in EL images very difficult.A self-reference scheme based on the Fourier image reconstruction technique is proposed for defect detection of solar cells with EL images. The target defects appear as line- or bar-shaped objects in the EL image. The Fourier image reconstruction process is applied to remove the possible defects by setting the frequency components associated with the line- and bar-shaped defects to zero and then back-transforming the spectral image into a spatial image. The defect region can then be easily identified by evaluating the gray-level differences between the original image and its reconstructed image. The reference image is generated from the inspection image itself and, thus, can accommodate random inhomogeneous backgrounds. Experimental results on a set of various solar cells have shown that the proposed method performs effectively for detecting small cracks, breaks, and finger interruptions. The computation time of the proposed method is also fast, making it suitable for practical implementation. It takes only 0.29 s to inspect a whole solar cell image with a size of 550×550 pixels.

Solar power is an attractive alternative source of electricity. Multicrystalline solar cells dominate the market share owing to their lower manufacturing costs. The surface quality of a solar wafer determines the conversion efficiency of the solar cell. A multicrystalline solar wafer surface contains numerous crystal grains of random shapes and sizes in random positions and directions with different illumination reflections, therefore resulting in an inhomogeneous texture in the sensed image. This texture makes the defect detection task extremely difficult. This paper proposes a wavelet-based discriminant measure for defect inspection in multicrystalline solar wafer images.The traditional wavelet transform techniques for texture analysis and surface inspection rely mainly on the discriminant features extracted in individual decomposition levels. However, these techniques cannot be directly applied to solar wafers with inhomogeneous grain patterns. The defects found in a solar wafer surface generally involve scattering and blurred edges with respect to clear and sharp edges of crystal grains in the background. The proposed method uses the wavelet coefficients in individual decomposition levels as features and the difference of the coefficient values between two consecutive resolution levels as the weights to distinguish local defects from the crystal grain background, and generates a better discriminant measure for identifying various defects in the multicrystalline solar wafers. Experimental results have shown the proposed method performs effectively for detecting fingerprint, contaminant, and saw-mark defects in solar wafer surfaces.

This paper presents a method of automatic defect inspection for the photovoltaic industry, with a special focus on multicrystalline solar wafers. It presents a machine vision-based scheme to automatically detect saw-mark defects in solar wafer surfaces. A saw-mark defect is a severe flaw that occurs when a silicon ingot is cut into wafers. Early detection of saw-mark defects in the wafer cutting process can reduce material waste and improve production yields. A multicrystalline solar wafer surface presents random shapes, sizes, and orientations of crystal grains in the surface, making the automatic detection of saw-mark defects extremely difficult. The proposed saw-mark detection scheme involves two main procedures: (1) Fourier image reconstruction to remove the multi-grain background of a solar wafer image and (2) a line detection process in the reconstructed image to locate saw-marks. The Fourier transform (FT) is used to eliminate crystal grain patterns and results in a non-textured surface in the reconstructed image. Since a saw-mark is presented horizontally in the sliced wafer, vertical scan lines in the reconstructed image are individually evaluated by a line detection process. A pixel far away from the line sought can then be effectively identified as a defect point. Experimental results show that the proposed method can effectively detect various saw-mark defects, specifically black lines, white lines, and impurities in multicrystalline solar wafers.

Purpose The purpose of this paper is to develop a robot vision system for surface defect detection of 3D objects. It aims at the ill‐defined qualitative items such as stains and scratches. Design/methodology/approach A robot vision system for surface defect detection may counter: high surface reflection at some viewing angles; and no reference markers in any sensed images for matching. A filtering process is used to separate the illumination and reflection components of an image. An automatic marker‐selection process and a template‐matching method are then proposed for image registration and anomaly detection in reflection‐free images. Findings Tests were performed on a variety of hand‐held electronic devices such as cellular phones. Experimental results show that the proposed system can reliably avoid reflection surfaces and effectively identify small local defects on the surfaces in different viewing angles. Practical implications The results have practical implications for industrial objects with arbitrary surfaces. Originality/value Traditional visual inspection systems mainly work for two‐dimensional planar surfaces such as printed circuit boards and wafers. The proposed system can find the viewing angles with minimum surface reflection and detect small local defects under image misalignment for three‐dimensional objects.

In this paper, we propose a Hough transform-based method to identify low-contrast defects in unevenly illuminated images, and especially focus on the inspection of mura defects in liquid crystal display (LCD) panels. The proposed method works on 1-D gray-level profiles in the horizontal and vertical directions of the surface image. A point distinctly deviated from the ideal line of a profile can be identified as a defect one. A 1-D gray-level pro- file in the unevenly illuminated image results in a nonstationary line signal. The most commonly used technique for straight line detection in a noisy image is Hough transform (HT). The standard HT requires a sufficient number of points lie exactly on the same straight line at a given parameter resolution so that the accumu- lator will show a distinct peak in the parameter space. It fails to detect a line in a nonstationary signal. In the proposed HT scheme, the points that contribute to the vote do not have to lie on a line. Instead, a distance tolerance to the line sought is first given. Any point with the distance to the line falls within the tolerance will be accumulated by taking the distance as the voting weight. A fast search procedure to tighten the possible ranges of line parameters is also proposed for mura detection in LCD images. Experimental results have shown that the proposed method can effectively detect various mura defects including spot-, line-, and region-mura. It performs well for the test images containing non- textured and structurally textured patterns in the unevenly illumi- nated surfaces. Index Terms—Defect detection, Hough transform (HT), liquid crystal display, mura, surface inspection.

It is important in image restoration to remove noise while preserving meaningful details such as edges and fine features. The existing edge-preserving smoothing methods may inevitably take fine detail as noise or vice versa. In this paper, we propose a new edge-preserving smoothing technique based on a modified anisotropic diffusion. The proposed method can simultaneously preserve edges and fine details while filtering out noise in the diffusion process. Since the fine detail in the neighborhood of a small image window generally have a gray-level variance larger than that of the noisy background, the proposed diffusion model incorporates both local gradient and gray-level variance to preserve edges and fine details while effectively removing noise. Experimental results have shown that the proposed anisotropic diffusion scheme can effectively smooth noisy background, yet well preserve edge and fine details in the restored image. The proposed method has the best restoration result compared with other edge-preserving methods.

In this paper, an anisotropic diffusion model with a generalized diffusion coefficient function is presented for defect detection in low-contrast surface images and, especially, aims at material surfaces found in liquid crystal display (LCD) manufacturing. A defect embedded in a low-contrast surface image is extremely difficult to detect because the intensity difference between unevenly-illuminated background and defective regions are hardly observable. The proposed anisotropic diffusion model provides a generalized diffusion mechanism that can flexibly change the curve of the diffusion coefficient function. It adaptively carries out a smoothing process for faultless areas and performs a sharpening process for defect areas in an image. An entropy criterion is proposed as the performance measure of the diffused image and then a stochastic evolutionary computation algorithm, particle swarm optimization (PSO), is applied to automatically determine the best parameter values of the generalized diffusion coefficient function. Experimental results have shown that the proposed method can effectively and efficiently detect small defects in low-contrast surface images.