SpringerLink
Log in

Solar cell surface defect inspection based on multispectral convolutional neural network

  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Agarwal, K., & Shivpuri, R. (2015). On line prediction of surface defects in hot bar rolling based on Bayesian hierarchical modeling. Journal of Intelligent Manufacturing,26(4), 785–800.

    Article  Google Scholar 

  • Anwar, S. A., & Abdullah, M. Z. (2014). Micro-crack detection of multi crystalline solar cells featuring an improved anisotropic diffusion filter and image segmentation technique. EURASIP Journal on Image and Video Processing,2014(1), 15.

    Article  Google Scholar 

  • Belouchrani, A., Abed-Meraim, K., Cardoso, J. F., & Moulines, E. (1997). A blind source separation technique using second-order statistics. IEEE Transactions on Signal Processing,45(2), 434–444.

    Article  Google Scholar 

  • Bianconi, F., González, E., Fernández, A., & Saetta, S. A. (2012). Automatic classification of granite tiles through colour and texture features. Expert Systems with Applications,39(12), 11212–11218.

    Article  Google Scholar 

  • Bulnes, F. G., Usamentiaga, R., Garcia, D. F., & Molleda, J. (2016). An efficient method for defect detection during the manufacturing of web materials. Journal of Intelligent Manufacturing,27(2), 431–445.

    Article  Google Scholar 

  • Çaydaş, U., & Ekici, S. (2012). Support vector machines models for surface roughness prediction in CNC turning of AISI 304 austenitic stainless steel. Journal of Intelligent Manufacturing,23(3), 639–650.

    Article  Google Scholar 

  • Chen, F. C., & Jahanshahi, M. R. (2018). NB-CNN: Deep learning-based crack detection using convolutional neural network and naive Bayes data fusion. IEEE Transactions on Industrial Electronics,65(5), 4392–4400.

    Article  Google Scholar 

  • Chen, S. H., & Perng, D. B. (2016). Automatic optical inspection system for IC molding surface. Journal of Intelligent Manufacturing,27, 1–12.

    Article  Google Scholar 

  • Chiou, Y. C., Liu, J. Z., & Liang, Y. T. (2011). Micro crack detection of multi-crystalline silicon solar wafer using machine vision techniques. Sensor Review,31(2), 154–165.

    Article  Google Scholar 

  • Deitsch, S., Christlein, V., Berger, S., Buerhop-Lutz, C., Maier, A., Gallwitz, F., et al. (2018). Automatic classification of defective photovoltaic module cells in electroluminescence images. ar**v preprint ar**v:1807.02894.

  • Ghorai, S., Mukherjee, A., Gangadaran, M., & Dutta, P. K. (2013). Automatic defect detection on hot-rolled flat steel products. IEEE Transactions on Instrumentation and Measurement,62(3), 612–621.

    Article  Google Scholar 

  • Glorot, X., Bordes, A., & Bengio, Y. (2011, June). Deep sparse rectifier neural networks. In Proceedings of the fourteenth international conference on artificial intelligence and statistics (pp. 315–323).

  • Jeguirim, S. E. G., Dhouib, A. B., Sahnoun, M., Cheikhrouhou, M., Schacher, L., & Adolphe, D. (2011). The use of fuzzy logic and neural networks models for sensory properties prediction from process and structure parameters of knitted fabrics. Journal of Intelligent Manufacturing,22(6), 873–884.

    Article  Google Scholar 

  • Jian, C., Gao, J., & Ao, Y. (2017). Automatic surface defect detection for mobile phone screen glass based on machine vision. Applied Soft Computing,52, 348–358.

    Article  Google Scholar 

  • Kang, S. (2018). Joint modeling of classification and regression for improving faulty wafer detection in semiconductor manufacturing. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-018-1447-2.

  • Kim, S., Kim, W., Noh, Y. K., & Park, F. C. (2017). Transfer learning for automated optical inspection. In 2017 International Joint Conference on Neural Networks (IJCNN) (pp. 2517–2524). IEEE.

  • Kumar, S. S., Abraham, D. M., Jahanshahi, M. R., Iseley, T., & Starr, J. (2018). Automated defect classification in sewer closed circuit television inspections using deep convolutional neural networks. Automation in Construction,91, 273–283.

    Article  Google Scholar 

  • Latif-Amet, A., Ertüzün, A., & Erçil, A. (2000). An efficient method for texture defect detection: Sub-band domain co-occurrence matrices. Image and Vision Computing,18(6–7), 543–553.

    Article  Google Scholar 

  • Li, W. C., & Tsai, D. M. (2012). Wavelet-based defect detection in solar wafer images with inhomogeneous texture. Pattern Recognition,45(2), 742–756.

    Google Scholar 

  • Liao, S., Law, M. W., & Chung, A. C. (2009). Dominant local binary patterns for texture classification. IEEE Transactions on Image Processing,18(5), 1107–1118.

    Article  Google Scholar 

  • Lin, H., Li, B., Wang, X., Shu, Y., & Niu, S. (2018). Automated defect inspection of LED chip using deep convolutional neural network. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-018-1415-x.

    Article  Google Scholar 

  • Liu, A., Lin, W., Paul, M., Deng, C., & Zhang, F. (2010). Just noticeable difference for images with decomposition model for separating edge and textured regions. IEEE Transactions on Circuits and Systems for Video Technology,20(11), 1648–1652.

    Article  Google Scholar 

  • Mirmehdi, M. (2008). Handbook of texture analysis. London: Imperial College Press.

    Book  Google Scholar 

  • Ordaz, M. A., & Lush, G. B. (2000, March). Machine vision for solar cell characterization. In Machine Vision Applications in Industrial Inspection VIII (Vol. 3966, pp. 238–249). International Society for Optics and Photonics.

  • Pierdicca, R., Malinverni, E. S., Piccinini, F., Paolanti, M., Felicetti, A., & Zingaretti, P. (2018). Deep convolutional neural network for automatic detection of damaged photovoltaic cells. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, XLII-2, 893–900.

  • Qian, X., Zhang, H., Zhang, H., Wu, Y., Diao, Z., Wu, Q. E., et al. (2017). Solar cell surface defects detection based on computer vision. International Journal of Performability Engineering,13(7), 1048.

    Google Scholar 

  • Silvén, O., Niskanen, M., & Kauppinen, H. (2003). Wood inspection with non-supervised clustering. Machine Vision and Applications,13(5–6), 275–285.

    Article  Google Scholar 

  • Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research,15(1), 1929–1958.

    Google Scholar 

  • Torres, P. J. R., Mercado, E. S., & Rifón, L. A. (2018). Probabilistic Boolean network modeling of an industrial machine. Journal of Intelligent Manufacturing,29(4), 875–890.

    Article  Google Scholar 

  • Tsai, D. M., Wu, S. C., & Li, W. C. (2012). Defect detection of solar cells in electroluminescence images using Fourier image reconstruction. Solar Energy Materials and Solar Cells,99, 250–262.

    Article  Google Scholar 

  • Wang, T., Chen, Y., Qiao, M., & Snoussi, H. (2018). A fast and robust convolutional neural network-based defect detection model in product quality control. International Journal of Advanced Manufacturing Technology,94(9–12), 3465–3471.

    Article  Google Scholar 

  • Weimer, D., Scholz-Reiter, B., & Shpitalni, M. (2016). Design of deep convolutional neural network architectures for automated feature extraction in industrial inspection. CIRP Annals,65(1), 417–420.

    Article  Google Scholar 

  • **an-Bao, W., Jie, L., Ming-Hai, Y., Wen-**u, H., & Yun-Tao, Q. (2014). Solar cells surface defects detection based on deep learning. Pattern Recognition and Artificial Intelligence,27(6), 517–523.

    Google Scholar 

  • Yao, M. H., Li, J., & Wang, X. B. (2013). Solar cells surface defects detection using RPCA method. Chinese Journal of Computers,36(9), 1943–1952.

    Article  Google Scholar 

  • Zhuang, F., Yanzheng, Z., Yang, L., Qixin, C., Mingbo, C., Jun, Z., et al. (2004). Solar cell crack inspection by image processing. In 2004 International Conference on Business of Electronic Product Reliability and Liability (pp. 77–80). IEEE.

Download references

Acknowledgements

This work is supported in part by National Natural Science Foundation (NNSF) of China under Grant 61873315, Natural Science Foundation of Hebei Province under Grant F2018202078, Science and Technology Program of Hebei Province under Grant 17211804D, Hebei Province Outstanding Youth Science Foundation F2017202062 and Young Talents Project in Hebei Province under Grant 210003.

Author information

Authors and Affiliations

  1. The School of Artificial Intelligence, Hebei University of Technology, Tian**, 300130, China

    Haiyong Chen, Yue Pang, Qidi Hu & Kun Liu

  2. Intelligent Rehabilitation Equipment and Detection Technology Engineering Research Center of Ministry of Education, Tian**, 300130, China

    Haiyong Chen & Kun Liu

Authors
  1. Haiyong Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yue Pang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qidi Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kun Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kun Liu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, H., Pang, Y., Hu, Q. et al. Solar cell surface defect inspection based on multispectral convolutional neural network. J Intell Manuf 31, 453–468 (2020). https://doi.org/10.1007/s10845-018-1458-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-018-1458-z

Keywords

Search

Navigation