Micro-crack detection of multicrystalline solar cells featuring an improved anisotropic diffusion filter and image segmentation technique

Anwar, Said Amirul; Abdullah, Mohd Zaid

doi:10.1186/1687-5281-2014-15

Cited by 105 publications

(61 citation statements)

References 22 publications

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“…This method can only identify defects on the edge of the cell and has no significant effect on internal defects. Anwar [18] proposed an improved anisotropic diffusion filter and image segmentation algorithm for the detection of micro-cracks in polycrystalline silicon solar cells with a detection result of 88%. This method is only applicable to micro-crack detection and cannot detect multiple defects.…”

Section: Related Work On Solar Cell Surface Detectionmentioning

confidence: 99%

Solar cell surface defect inspection based on multispectral convolutional neural network

et al. 2018

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Similar and indeterminate defect detection of solar cell surface with heterogeneous texture and complex background is a challenge of solar cell manufacturing. The traditional manufacturing process relies on human eye detection which requires a large number of workers without a stable and good detection effect. In order to solve the problem, a visual defect detection method based on multi-spectral deep convolutional neural network is designed in this paper. Firstly, a selected convolutional neural network (CNN)model is established. By adjusting the depth and width of the model, the influence of model depth and kernel size on the recognition result is evaluated. The optimal convolutional neural network model structure is selected. Secondly, the light spectrum features of solar cell color image are analyzed. It is found that a variety of defects exhibited different distinguishable characteristics in different spectral bands. Thus, a multi-spectral convolutional neural network model is constructed to enhance the discrimination ability of the model to distinguish between complex texture background features and defect features. Finally, some experimental results and K-fold cross validation show that the multi-spectral deep convolutional neural network model can effectively detect the solar cell surface defects with higher accuracy and greater adaptability. The accuracy of defect recognition reaches 94.30%. Applying such an algorithm can increase the efficiency of solar cell manufacturing and make the manufacturing process smarter.

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Section: Related Work On Solar Cell Surface Detectionmentioning

confidence: 99%

Solar cell surface defect inspection based on multispectral convolutional neural network

et al. 2018

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“…Otsu thresholding method – block binarization (Anwar & Abdullah, ; Qi et al ., ) was also applied in this paper to make binarization. By calculating an interclass variance between a background and objectives, the two areas can be separated.…”

Section: Image Processing Algorithmsmentioning

confidence: 99%

Qualitative and quantitative interpretation of SEM image using digital image processing

Saladra

Kopernik

2016

Journal of Microscopy

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The aim of the this study is improvement of qualitative and quantitative analysis of scanning electron microscope micrographs by development of computer program, which enables automatic crack analysis of scanning electron microscopy (SEM) micrographs. Micromechanical tests of pneumatic ventricular assist devices result in a large number of micrographs. Therefore, the analysis must be automatic. Tests for athrombogenic titanium nitride/gold coatings deposited on polymeric substrates (Bionate II) are performed. These tests include microshear, microtension and fatigue analysis. Anisotropic surface defects observed in the SEM micrographs require support for qualitative and quantitative interpretation. Improvement of qualitative analysis of scanning electron microscope images was achieved by a set of computational tools that includes binarization, simplified expanding, expanding, simple image statistic thresholding, the filters Laplacian 1, and Laplacian 2, Otsu and reverse binarization. Several modifications of the known image processing techniques and combinations of the selected image processing techniques were applied. The introduced quantitative analysis of digital scanning electron microscope images enables computation of stereological parameters such as area, crack angle, crack length, and total crack length per unit area. This study also compares the functionality of the developed computer program of digital image processing with existing applications. The described pre- and postprocessing may be helpful in scanning electron microscopy and transmission electron microscopy surface investigations.

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“…Chen et al [11] generated an enhanced saliency map using a novel steerable evidence filter then applied morphological operations to accomplish the segmentation of solar cell EL defects. Anwar and Abdullah [12] detects and segment micro cracks in polycrystalline solar cell images using a method based on anisotropic diffusion. The method performs well but didn't consider the cracks submerged with background.…”

Section: Introductionmentioning

confidence: 99%

Defects Inspection in Polycrystalline Solar Cells Electroluminescence Images Using Deep Learning

Rahman

Chen

2020

IEEE Access

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Solar cells defects inspection plays an important role to ensure the efficiency and lifespan of photovoltaic modules. However, it is still an arduous task because of the diverse attributes of electroluminescence images, such as indiscriminative complex background with extremely unbalanced defects and various types of defects. In order to deal with these problems, this paper proposes a new precise and accurate defect inspection method for photovoltaic electroluminescence (EL) images. The proposed algorithm leverages the advantage of multi attention network to efficiently extract the most important features and neglect the nonessential features during training. Firstly, we designed a channel attention to exploit contextual representations and spatial attention to effectively suppress background noise. Secondly, we incorporate both attention networks into modified U-net architecture and named it multi attention U-net (MAU-net) to extract effective multiscale features for defects inspection. Finally, we propose a hybrid loss which combines focal loss and dice loss aiming to solve two problems: a) overcome the class imbalance problem, and b) allowing the network to train with irregular image labels for some complex defects. The proposed multi attention U-net is evaluated on real photovoltaic EL images datasets using 5-fold cross validation technique. Experimental results demonstrate that the proposed network can segment and detect various complex defects correctly. The proposed method achieved the mean intersection over-union (m-IOU) of 0.699 and F-measure of 0.799 which outperforms the previous methods.

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Micro-crack detection of multicrystalline solar cells featuring an improved anisotropic diffusion filter and image segmentation technique

Cited by 105 publications

References 22 publications

Solar cell surface defect inspection based on multispectral convolutional neural network

Solar cell surface defect inspection based on multispectral convolutional neural network

Qualitative and quantitative interpretation of SEM image using digital image processing

Defects Inspection in Polycrystalline Solar Cells Electroluminescence Images Using Deep Learning

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