Solar Cell Busbars Surface Defect Detection Based on Deep Convolutional Neural Network
Keywords:
Convolutional neural networks, Deep Learning, Solar Cell, Surface DefectAbstract
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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K. Ranabhat, L. Patrikeev, A. A. Revina, K. Andrianov, V. Lapshinsky, E. Sofronova, ‘An introduction to solar cell technology’, Journal of Applied Engineering Science, pp. 481-491, Jan. 2016.
K. Fukuda, K. Yu, T. Someya, “The Future of Flexible Organic Solar Cells”, Advanced Energy Materials, May. 2020.
Y. Wang, L. Li, J. Xu, Y. Jia, J. Hong, X. Hu, G. Weng, X. Luo, S. Chen, Z. Zhu, J. Chu, H. Akiyama, ‘Adaptive automatic solar cell defect detection and classification based on absolute electroluminescence imaging’, Energy, August. 2021.
J. Du, X. Zhang, W. Ou, “Knowledge-infused process monitoring for quality improvement in solar cell manufacturing processes”, Journal of Quality Technology, Oct. 2021
J. Du, X. Zhang, J. Shi, “A Condition Change Detection Method for Solar Conversion Efficiency in Solar Cell Manufacturing Processes”, IEEE Transactions on Semiconductor Manufacturing, pp. 82-92, Oct. 2019.
C.S. Koyuncu, E. Aydemir and A.C. Başarır, “Selection Industry 4.0 maturity model using fuzzy and intuitionistic fuzzy TOPSIS methods for a solar cell manufacturing company”, Soft Computing, pp. 10335–10349, April 2021.
F. Zhao, X. Xu, S.Q. Xie, ‘Computer-Aided Inspection Planning-The state of the art’, Computers in Industry, pp. 453-466, Sept. 2009.
R. Hao, B. Lu, Y. Cheng, X. Li, B. Huang, “A steel surface defect inspection approach towards smart industrial monitoring”, Journal of Intelligent Manufacturing, pp. 1833-1843, Sept. 2018.
Q. Zhou, R. Chen, B. Huang, C. Liu, J. Yu, X. Yu, ‘An automatic surface defect inspection system for automobiles using machine vision methods’, Sensors (Switzerland), pp. 644-652, Feb. 2019
Z. Pengfei, P. Cao, Y. Yang, P. Guo, S. Chen, D. Zhang, ‘Simulation of a machine vision system for reflective surface defect inspection based on ray tracing’, Applied Optics, Feb. 2020.
A. Oleff, B. Küster, M. Stonis, L. Overmeyer, ‘Optical quality control for material extrusion based additive manufacturing’, ZWF Zeitschrift fuer Wirtschaftlichen Fabrikbetrieb, pp. 705-730, May 2021.
F. Wiesinger, D. Klepatsch, M. Bogner, ‘Optical quality control using deep learning’, in 31st European Modeling and Simulation Symposium, EMSS 2019, Sept. 2019.
M. Kassubeck, T. Malek, M. Mühlhausen, M. Kappel, S. Castillo, M. A. Dittrich, M. Magnor, ‘Optical Quality Control for Adaptive Polishing Processes’, in Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation, May. 2020.
K. R. Cadena, F. N. G. Ramos, ‘Artificial neuronal networks for optical inspection in PCB quality control’, Visión electrónica, pp. 169-178, Nov. 2017.
M. A. Green, “The path to 25% silicon solar cell efficiency: History of silicon cell evolution”, Progress in Photovoltaics: Research and Applications, pp. 183-189, March 2009.
Y. Xing, P. Han, P. Liang, S. Lou, Y. Zhang, S. Hu, H. Zhu, C. Zhao, Y. Mi, ‘A review of concentrator silicon solar cells’, Renewable and Sustainable Energy Reviews, pp. 1697-1708, Nov. 2015.
X. Yang, K. Weber, Z. Hameiri, S.D. Wolf, ‘Industrially feasible, dopant-free, carrier-selective contacts for high-efficiency silicon solar cells’, Progress in Photovoltaics: Research and Applications, pp. 11-25, May. 2017.
Y. Liu, Y. Li, G. Yang, L. Mazzarella, P. P. Moya, A.C. Tamboli, K. Weber, M. Boccard, O. Isabella, X. Yang, B. Sun, ‘High-Efficiency Silicon Heterojunction Solar Cells: Materials, Devices and Applications’, Materials Science and Engineering R: Reports, Oct. 2020.
C. Borri, M. Gagliardi, M. Paggi, “Fatigue crack growth in Silicon solar cells and hysteretic behaviour of busbars”, Solar Energy Materials and Solar Cells, pp. 21-29, July 2018.
V. Carollo, D. Piga, C. Borri, M. Paggi, “Identification of elasto-plastic and nonlinear fracture mechanics parameters of silver-plated copper busbars for photovoltaics”, Engineering Fracture Mechanics, pp. 439-454, Nov. 2019.
N. Chen, A. Ebong, “Towards 20% efficient industrial Al-BSF silicon solar cell with multiple busbars and fine gridlines”, Solar Energy Materials and Solar Cells, pp. 107-113, March. 2016.
I.R. Fermo, T.S. Cavali, L. B. Rocha, C. L. Strutkoske, F. C. Flores, C.M. G. Andrade, “Development of a low-cost digital image processing system for oranges selection using hopfield networks”, Food and Bioproducts Processing, pp. 181-192, Jan. 2021.
P. C. P. Junior, A. Monteiro, R. D. L. Ribeiro, A. C. Sobieranski, A. V. Wangenheim, “Comparison of classical computer vision vs. Convolutional neural networks for weed mapping in aerial images”, Revista de Informatica Teorica e Aplicada, Dec. 2020.
X. Wu, D. Sahoo, S. C. H. Hoi, ‘Recent advances in deep learning for object detection’, Neurocomputing, pp. 39-64, July, 2020.
S. Zhang, L. Yao, A. Sun, Y. Tay, ‘Deep learning based recommender system: A survey and new perspectives’, ACM Computing Surveys, July. 2017.
C. Chen, C. Qin, H. Qiu, G. Tarroni, J. Duan, W. Bai, D. Rueckert “Deep Learning for Cardiac Image Segmentation: A Review”, Frontiers in Cardiovascular Medicine, pp. 25-40, Mar. 2020.
J. Gu, Z. Wang, J. Kuen, L. Ma, A. Shahroudy, B. Shuai, T. Liu, X. Wang, G. Wang, J. Cai, T. Chen, “Recent advances in convolutional neural networks”, Pattern Recognition, pp. 354-377, May. 2018.
A. Khan, A. Sohail, U. Zahoor, A. Qureshi, ‘A survey of the recent architectures of deep convolutional neural networks’, Artificial Intelligence Review, Jan. 2020.
B. Wang, Y. Sun, B. Xue, M. Zhang, ‘Evolving Deep Convolutional Neural Networks for Image Classification’, IEEE Transactions on Evolutionary Computation, Oct. 2018.
X. Zhao, M. Zhang, J. Zhang, ‘Ensemble learning-based CNN for textile fabric defects classification’, International Journal of Clothing Science and Technology, Jan. ‘2021.
E. Cortés and S. Sánchez, “Deep Learning Transfer with AlexNet for chest X-ray COVID-19 recognition”, IEEE LAT AM T, vol. 19, no. 6, pp. 944–951, Jun. 2021.
S. Y. Lee, B. A. Tama, S. J. Moon, S. Lee, (2019) ‘Steel surface defect diagnostics using deep convolutional neural network and class activation map’, Applied Sciences (Switzerland), Dec. 2019.
C. Yang, S. Chen, Y. Wang, J. Zhang, ‘The Evaluation of DCNN on Vector-SIMD DSP’, IEEE Access, pp. 99-108, Feb. 2019.
X. Wu, K. Cao, X. Gu, ‘A surface defect detection based on convolutional neural network’, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 185-194, Oct. 2017.
X. Tao, D. Zhang, W. Ma, X. Liu, D. Xu, ‘Automatic metallic surface defect detection and recognition with convolutional neural networks’, Applied Sciences (Switzerland), Sept. 2018.
M. Zhang, J. Wu, H. Lin, P. Yuan, Y. Song, ‘The Application of One-Class Classifier Based on CNN in Image Defect Detection’, in Procedia Computer Science, pp. 341-348, Dec, 2017.
B. Qin, L. Liang, J. Wu, Q. Quan, Z. Wang, D. Li, ‘Automatic identification of down syndrome using facial images with deep convolutional neural network’, Diagnostics, July. 2020.
Y. Shu, B. Li, H. Lin, ‘Quality safety monitoring of LED chips using deep learning-based vision inspection methods’, Measurement: Journal of the International Measurement Confederation, pp. 108-123, Sept. 2020.
B. Hu, J. Wang, ‘Detecting LED Chip Surface Defects with Modified Faster R-CNN’, in, pp. 99-108, June. 2020.
S. Tian, W. Li, S. Li, G. Tian, L. Sun, X. Niang, ‘Image Defect Detection and Segmentation Algorithm of Solar Cell Based on Convolutional Neural Network’, in 2021 IEEE 6th International Conference on Intelligent Computing and Signal Processing, ICSP 2021, pp. April. 2021.
B. Su, H. Chen, P. Chen, G. B. Bian, K. Liu, W. Liu, ‘Deep Learning-Based Solar-Cell Manufacturing Defect Detection with Complementary Attention Network’, IEEE Transactions on Industrial Informatics, pp. 99-108, July. 2020.
H. Chen, Y. Pang, Q. Hu, K. Liu, “Solar cell surface defect inspection based on multispectral convolutional neural network”, Journal of Intelligent Manufacturing, pp. 453-468, Dec. 2020.
A. Bartler, L. Mauch, B. Yang, M. Reuter, L. Stoicescu. (2018) “Automated detection of solar cell defects with deep learning”, in European Signal Processing Conference, pp. 2035-2039, Sept. 2018.
N. Moroney, ‘Local color correction using non-linear masking’, in Final Program and Proceedings - IS and T/SID Color Imaging Conference, Jan. 2000.
Q. Zhou, R. Chen, B. Huang, C. Liu, J. Yu, X. Yu, ‘An automatic surface defect inspection system for automobiles using machine vision methods’, Sensors (Switzerland), pp. 644-652, Feb. 2019.
