Solar cell surface defect inspection based on multispectral convolutional neural network

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 detect...

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Bibliographic Details
Published in:Journal of intelligent manufacturing 2020-02, Vol.31 (2), p.453-468
Main Authors: Chen, Haiyong, Pang, Yue, Hu, Qidi, Liu, Kun
Format: Article
Language:English
Subjects:
Adaptability
Artificial neural networks
Business and Management
Control
Defects
Inspection
Machines
Manufacturing
Manufacturing industry
Mechatronics
Neural networks
Photovoltaic cells
Processes
Production
Recognition
Robotics
Solar cells
Spectra
Spectral bands
Surface defects
Surface layers
Texture
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Summary: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 (CNN) is designed in this paper. Firstly, a selected 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 CNN 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 CNN 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 CNN 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.
ISSN:0956-5515
1572-8145
DOI:10.1007/s10845-018-1458-z