- Open Access
Micro-crack detection of multicrystalline solar cells featuring an improved anisotropic diffusion filter and image segmentation technique
© Anwar and Abdullah; licensee Springer. 2014
- Received: 23 April 2013
- Accepted: 3 March 2014
- Published: 21 March 2014
This paper presents an algorithm for the detection of micro-crack defects in the multicrystalline solar cells. This detection goal is very challenging due to the presence of various types of image anomalies like dislocation clusters, grain boundaries, and other artifacts due to the spurious discontinuities in the gray levels. In this work, an algorithm featuring an improved anisotropic diffusion filter and advanced image segmentation technique is proposed. The methods and procedures are assessed using 600 electroluminescence images, comprising 313 intact and 287 defected samples. Results indicate that the methods and procedures can accurately detect micro-crack in solar cells with sensitivity, specificity, and accuracy averaging at 97%, 80%, and 88%, respectively.
- Micro-crack detection
- Multicrystalline solar cell
- Image segmentation
- Anisotropic diffusion
- Angular radial transform
- Support vector machine
The increasing demand for solar electrical energy has multiplied the need for photovoltaic (PV) arrays. As the major component of the PV array, the demand for solar cells has also increased. This demand has translated into an increased production of solar cells in recent years. Depending on the materials used in manufacturing, solar cells can be divided into two major types. They are (i) monocrystalline and (ii) multicrystalline silicones. Due to low manufacturing and processing cost of the multicrystalline silicon, this material is generally more preferred in the production of the solar wafer or PV module. There is great potential for the automation in solar cell industry because millions of solar cells are manufactured daily worldwide. According to recent statistics, the growth rate of the solar PV module reached a record high in 2011, generating more than US$93 billion in revenue with multicrystalline cells constituting more than 50% of the world production . Although many operations in the PV industry have been automated, the inspection and grading processes continue to be based on manual or semi-manual efforts.
Finished solar cells are occasionally found to be defective or faulty. The defects fall into two groups: (i) intrinsic and (ii) extrinsic. Grain boundaries are an example of intrinsic defect, while micro-cracks belong to the second category. The former type of defects diminish the short-circuit current of the cell, and this leads to loss in the efficiency. The latter defects form a class of cracks that are entirely invisible to the naked eye. With dimensions smaller than 30 μm , this type of defect can only be visualized electronically like using the electroluminescence (EL) technique and high-resolution cameras.
In practice, there are various shapes and sizes of micro-cracks in a solar cell depending on how they are formed. For example a line-shaped micro-crack is caused by scratches, and it generally occurs during cell fabrication . This type of defect can also be due to wafer sawing or laser cutting, which propagates and causes the detachment or internal breakage of the grainy materials within the solar cells . In contrast, star-shaped micro-crack is formed due to a sharp point impact which induces several line cracks with a tendency to cross each other . There are other types of micro-crack defects, but these two are the most commonly found in solar cell production. Köntges et al.  reported that there may be a risk of failure for PV modules containing cells that have micro-cracks or other types of defects. Hence, it is important to have high-quality, defect-free cells in the production of PV modules.
To date, few studies have highlighted the benefit of computer inspection for defect detection in EL images of solar cells. For example, multicrystalline solar cell images have been categorized into three distinct classes based on the features extracted from texture analysis . An evaluation of crack formation in the PV module before and after mechanical load testing using EL images has been presented by Kajari-Schröder et al. . Recently, a defect detection scheme based on Fourier image reconstruction has also been reported . These authors presented a successful detection of a micro-crack which is geometrically simple like straight lines. A micro-crack detection scheme for a solar wafer based on an anisotropic diffusion filter has also been documented . As reported by these authors, this filter is very efficient in preserving important edges in the image while smoothing other less important and connected regions. However, correct implementation of this technique depends crucially on the choice of an edge stopping threshold. In most cases, this value has to be determined interactively, frequently through trail-and-error method. Only under very unusual circumstances can anisotropic diffusion filtering be successful using a single threshold since images are likely to be gray level variations in objects and background due to non-uniform lighting and other factors. Clearly, a more robust approach is needed in order to increase the efficiency of this filtering strategy. In this paper, an enhanced version of the anisotropic diffusion filter featuring an adaptive thresholding via a sigmoid transformation function is presented. Meanwhile, pattern classification is established using support vector machines (SVMs) with supervised learning . The methods and procedures are tested using intact and defected solar cells, and results are compared with other filters and artificial classifiers.
2.1 Electroluminescence image
In this study, a series of image processing procedures are performed, capitalizing the unique textural properties and multicrystalline grain inhomogeneity of the solar cell. The details are described in the next section.
2.2 Image pre-processing
As seen in Figures 1 and 3, the EL images of the solar cell contain various features, such as fingers (horizontal lines) that are periodic in nature and perpendicular to the bus-bar (thicker vertical lines in Figure 1a (i) and Figure 1b (i)). A close inspection of these figures revealed that the intensity distribution is not uniform both within the cell and among the cells. The presence of the broken fingers and non-uniform background luminescence directly affects the micro-crack analysis, especially if a simple image segmentation technique is used. The solutions to these problems are to remove the periodic interruption of fingers and minimize the effect on background inhomogeneity on image processing. This can be done by filtering in the frequency domain.
2.3 Anisotropic diffusion filtering
This subsection presents an implementation of anisotropic diffusion filtering for image enhancement. As can be seen in Figure 4d (ii), the micro-crack pixels are characterized with low gray scale values but high gradients. The convolution of I e (x, y) with a simple edge detector (e.g., Sobel kernel) will yield high and low gradients at the edges and micro-crack pixels, respectively. Consequently, the result is that the produced image contains two lines, corresponding to regions with high and low intensity gradients. This will give rise to the difficulty in the detection leading to many false negatives. We solved this problem by means of the anisotropic diffusion filtering, which produces equal response to any pixels, including the micro-crack areas. In order to achieve this, the diffusion filter is programmed to take into account not only the intensity of the gradient but also the intensity of the gray level of each pixel. The details are explained below.
These diffusion coefficients exhibit a low value at high gradient purposely to preserve the corresponding edges. On the other hand, these coefficients produce high value at low gradient indicating a strong smoothing effect on the pixels involved. Thus, the anisotropic diffusion filtering will produce a smoothed image while the important edges are preserved. Parameter K appearing in Equations 4 and 5 is an edge stopping threshold, and it needs to be correctly specified in order to ensure a successful application of this filtering strategy. If K is too small, then the diffusion process will be terminated earlier, resulting in I d (x, y, t) which is approximately equal to I d (x, y, 0). In contrast, fixing K too large will significantly diffuse the image, resulting in image blurring. Therefore, the choice of the parameter K is important for producing a diffused image that retains the important edges while smoothing the other regions of the image.
As seen in Figure 6, the response of the diffusion coefficient varies with the different threshold values. The response is more sensitive when the threshold value is low with respect to the same gradient s. High value of the coefficient yields a high diffusivity for the corresponding pixel in the image which leads to blurring effect. As mentioned earlier, existing techniques only used a single edge stopping threshold value for the whole image. In this study, an adaptive edge stopping threshold function given in Equation 8 is used. This resulted in different threshold values for different pixels depending on their gray scale values through a mapping process.
In this study, the proposed anisotropic diffusion filtering is performed in three steps. First, the filtered image, I e (x, y), is smoothed using a 2-D Gaussian filter of size 5 × 5 yielding I d (x, y, 0). Second, the smoothed image is then processed using Equation 8 to produce the edge stopping threshold matrix, g(x, y), which in turn is used to calculate the diffusion coefficient function given by Equation 7. Third, Equation 3 is invoked and the calculation is terminated after a few iterations. In this case, the iteration number is determined heuristically and is usually less than 10 in most cases.
where μ and σ are the mean and the standard deviation of the gray level intensity of the input image, respectively, and α is a scaling factor.
Next, the intensity tracing and thresholding are performed on B F using I e (x, y) as the reference image. The purpose of this procedure is to further reduce the noise or the unwanted shapes, such as scratches, dislocation clusters, or grain boundaries. The gray values of these artifacts are relatively higher compared to those of the micro-crack pixels. This procedure helps to improve the feature extraction because it significantly reduces the number of shapes.
2.5 Shape analysis
In this section, the experimental results from the methods and procedures described in the above sections are presented. This includes the image segmentation and classification. All experiments are performed on a desktop computer equipped with a dual core 2.80 GHz processor, 2 GB of RAM, and an installed MATLAB software package. The results obtained in this section are based on 600 samples of which 313 are good samples and the remaining are defected or cracked cells.
3.1 Image processing
where ℓ GT is the number of micro-crack pixels in the corresponding ground truth image, ℓ r is the number of pixels in the segmented image which matches the ground truth micro-crack pixels, and ℓ N is the total number of extracted pixels in the segmented image. Examples of ground truth images corresponding to defected cells in Figure 15a (i-iv) are shown in Figure 15h (i-iv), respectively. On the other hand, the cpt index indicates the completeness of the segmentation technique in detecting micro-crack pixels in the defected solar cells. Clearly, from Equation 11, cpt is equal to 1 if ℓ r = ℓ GT , indicating the perfect match between the number of micro-crack pixels detected by the algorithm and the ground truth image. In contrast, cpt is equal to 0 if there is no match. Meanwhile, the crt index measures the correctness of the segmented image produced. Similarly, this index is equal to 1 if the segmented image matches the ground truth. Practically, ℓ r ≤ ℓ N since micro-crack as well as noise pixels are also detected. Hence crt also ranges from 0 to 1. Calculating cpt and crt enables the F-measure to be computed using Equation 10. In this case, the higher the F-measure, the better the image segmentation.
Completeness and correctness measures of the segmentation results
3.2 Shape classification
For comparison purpose, the scattered plots of shape features produced by the well-known methods like (i) the Fourier descriptor (FD) , (ii) the generic Fourier descriptor (GFD) , and (iii) the projection-based Radon composite features (RCF)  are also included in this figure. A close examination of Figure 20 shows that the overlap between micro-crack and other arbitrary shapes is more prominent in Figure 20b,c,d than in Figure 20a. All micro-crack shapes in Figure 20b,c,d occupy the regions that are enclosed within other arbitrary shapes. Clearly, there is no unique demarcation between these two groups in the PCA space. Hence, any attempt to use FD, GFD, or RCF as features in the classification scheme would result in many samples being misclassified. In contrast, the overlap between the groups is less prominent for ART features, as shown in Figure 20a. It can be seen that the other arbitrary shapes are skewed to the right, whereas the micro-crack shapes are skewed to the left. Therefore, it is hypothesized that the features extracted using ART are more separable compared to those extracted using FD, GFD, and RCF.
Distribution of intact and defected cells in the dataset
The classification results of the testing set
For completeness, SVM experiments were repeated using FD, GFD, and RCF shape descriptors, and the results are also given in Table 3. Clearly, ART outperformed other shape descriptors in all assessment metrics. This again demonstrated that ART gives the best discriminating ability when dealing with this type of shape classification problem compared to other shape descriptors. In addition, the average processing time for each EL image is approximately 4.1 s which is comparable to the semi-manual inspection by a human expert. Meanwhile, the smallest micro-crack detected by the proposed algorithm is 47 pixels in size which physically corresponds to 6.22 mm in length.
The early detection of micro-cracks in solar cells is important in the production of PV modules. In this study, an image processing scheme composed of segmentation procedures based on anisotropic diffusion and shape classification is presented. The results show that the segmentation procedures can detect and identify micro-crack pixels efficiently in the presence of various forms of noise. The anisotropic diffusion filtering with gray level-based diffusion coefficient proposed in this study produced excellent enhancement and improved segmentation. The advantage of this filtering technique is its ability to enhance the pixels with low gray scale and high gradient such as the micro-crack defects in solar cell. Trained with SVM using 240 samples, this artificial classifier produced a correct classification rate of consistently higher than 88% with average sensitivity and specificity of 97.7% and 80.2%, respectively. These results are very promising as it demonstrates a first attempt of integrated image processing and machine learning platform toward its eventual application of micro-crack inspection of solar cells.
This work is supported by the Malaysia Collaborative Research in Engineering, Science and Technology Centre (CREST) 304/PELECT/6050264/C121.
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