Solar Cell Busbars Surface Defect Detection Based on Deep Convolutional Neural Network



Convolutional neural networks, Deep Learning, Solar Cell, Surface Defect


Defect detection of the solar cell surface with texture and complicated background is a challenge for solar cell manufacturing. The classic manufacturing process relies on human eye detection, which requires many workers without a steady and good detection effect. In order to solve the problem, a visual defect detection method based on a new deep convolutional neural network (CNN) is designed in this paper. First, we develop a CNN model by adjusting the depth and width of the model. Then, the optimal CNN model structure is developed by comparing the performance of different depth and width combinations. This research focuses on finding a way to distinguish defects in solar cells from the background texture of busbars and fingers. The characteristics of solar cell color images are analyzed. We find that defects exhibited different distinguishable characteristics in various structures. The deep CNN model is constructed to enhance the discrimination capacity of the model to distinguish between complicated texture background features and defect features. Finally, some experimental results and K-fold cross-validation show that the new deep CNN model can detect solar cell surface defects more effectively than other models. The accuracy of defect recognition
reaches 85.80%. In solar cell manufacturing, such an algorithm can increase the productivity of solar cell manufacturing and make the manufacturing process smarter.


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Author Biographies

Yavuz Selim Balcıoğlu, Gebze Technical University

Yavuz Selim Balcıoğlu is a lecturer at the University of Gebze Technical. He received his BS degrees in Economy from the University of Kocatepe and MS degrees in Business Administration from the University of Yasar in 2005 and 2014, respectively, and doing a Ph.D. in business analytics from the University of Gebze Technic. He is the author of more than 40 journal papers and has 13 International awards in robotics. His current research interests include computer vision and robotics, neural networks.

Bulent Sezen, Gebze Technical University

Bülent Sezen is a professor at the University of Gebze Technical. He received his BS degrees in Industrial Engineering from the University of Istanbul Technical and MS degrees in Industrial Engineering from the University of Virginia Tech in 1997. He finished a Ph.D. degree in Business Administration from the University of Gebze Technic. He is the author of more than 50 journal papers.

Ceren Cubukcu Cerasi, Gebze Technical University

Ceren Cubukcu Cerasi received her Bachelor’s in Information Systems Engineering from the dual-diploma program of Binghamton University, Binghamton, NY, USA and Istanbul Technical University, Istanbul, Turkey in 2008. She completed her MBA from the Bentley University, Waltham, MA, USA in 2011. After her MBA, she worked as a Business Systems Analyst in Greater Boston Area for two years. She completed her PhD in Informatics at the Istanbul University, Istanbul, Turkey in 2018. She won an award for best study in the field of innovation with her PhD thesis in 2019. She has publications in the fields of digital entrepreneurship, fuzzy systems, software development and system analysis. She is working as an Assistant Professor at the Management Information Systems department of Gebze Technical University located in Kocaeli, Turkey.


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How to Cite

Balcıoğlu, Y. S., Sezen, B., & Cubukcu Cerasi, C. . (2022). Solar Cell Busbars Surface Defect Detection Based on Deep Convolutional Neural Network. IEEE Latin America Transactions, 21(2), 242–250. Retrieved from