Solar Cell Surface Defects Detection based on Computer Vision

Qian, Xiaoliang

doi:10.23940/ijpe.17.07.p6.10481056

Cited by 10 publications

(6 citation statements)

References 31 publications

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“…Unfortunately, this method only has significant effect on linear features and performs poorly on other defections in the image with visible light spectrum. Qian et al [21]. reviewed the typical types of solar cell surface defects and evaluated current popular machine vision detection algorithms.…”

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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“…There have been several attempts towards detecting defects in printed electronic devices and microelectromechanical systems (MEMS) based on NDT methodologies. Attempts include [9,10] implementing a machine-learning (ML) automatic defect recognition (ADR) approach with a statistically significant accuracy. Nonetheless, ML-based approaches, especially deep learning techniques, require a large-size dataset and significant annotated and labelled data.…”

Section: Introductionmentioning

confidence: 99%

A Computer Vision-Based Quality Assessment Technique for R2R Printed Silver Conductors on Flexible Plastic Substrates

Amini

Gan

2023

Applied Sciences

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The demand for flexible large-area optoelectronic devices has been growing significantly during recent years. Roll-to-roll (R2R) printing facilitates the cost-efficient industrial production of different optoelectronic devices. Nonetheless, the performance of these devices is highly dependent on the printing quality and number of defects of R2R printed conductors. The image processing technique is an efficient nondestructive testing (NDT) methodology used to detect such defects. In this study, a computer vision-based assessment tool was utilized to visualize R2R printed silver conductors’ defects on flexible plastic substrates. A multistage defect detection technique was proposed to detect and classify both printing-induced defects and imperfections as well as the misalignment of the printed conductors with respect to the reference design. The method proved to be a very reliable approach that can be used independently or in conjunction with electrical testing methods for quality assurance purposes during the production of R2R prints.

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“…However, the dataset used in this method is small. In another research [19], the author employs a deep belief network for defect detection in PV cells.…”

Section: Introductionmentioning

confidence: 99%

Photovoltaic cell defect classification using convolutional neural network and support vector machine

Ahmad

Jin

Zhu

et al. 2020

IET Renewable Power Generation

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Solar Cell Surface Defects Detection based on Computer Vision

Cited by 10 publications

References 31 publications

Solar cell surface defect inspection based on multispectral convolutional neural network

Solar cell surface defect inspection based on multispectral convolutional neural network

A Computer Vision-Based Quality Assessment Technique for R2R Printed Silver Conductors on Flexible Plastic Substrates

Photovoltaic cell defect classification using convolutional neural network and support vector machine

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