Open Access
EPJ Photovolt.
Volume 13, 2022
Article Number 27
Number of page(s) 12
Section Modules and Systems
Published online 06 December 2022
  1. T. Fuyuki, A. Kitiyanan, Photographic diagnosis of crystalline silicon solar cells utilizing electroluminescence, Appl. Phys. A 96, 189 (2009) [CrossRef] [Google Scholar]
  2. K. Bedrich, M. Bokalic, M. Bliss, M. Topic, T.R. Betts, R. Gottschalg, Electroluminescence imaging of PV devices: advanced vignetting calibration, IEEE J. Photovolt. 8, 1297 (2018) [CrossRef] [Google Scholar]
  3. W.S.M. Brooks, D.A. Lamb, S.J.C. Irvine, IR reflectance imaging for crystalline Si solar cell crack detection, IEEE J. Photovolt. 5, 1271 (2015) [CrossRef] [Google Scholar]
  4. I. Zafirovska, M.K. Juhl, J.W. Weber, J. Wong, T. Trupke, Detection of finger interruptions in silicon solar cells using line scan photoluminescence imaging, IEEE J. Photovolt. 7, 1496 (2017) [CrossRef] [Google Scholar]
  5. T.W. Teo, Z. Mahdavipour, M.Z. Abdullah, Recent advancements in micro-crack inspection of crystalline silicon wafers and solar cells, Measur. Sci. Technol. 31, 081001 (2020) [CrossRef] [Google Scholar]
  6. D.M. Tsai, C.C. Chang, S.M. Chao, Micro-crack inspection in heterogeneously textured solar wafers using anisotropic diffusion, Image Vis. Comput. 28, 491 (2010) [CrossRef] [Google Scholar]
  7. S.A. Anwar, M.Z. Abdullah, Micro-crack detection of multicrystalline solar cells featuring an improved anisotropic diffusion filter and image segmentation technique, EURASIP J. Image Video Process. 2014, 15 (2014) [CrossRef] [Google Scholar]
  8. D.-C. Tseng, Y.-S. Liu, C.-M. Chou, Automatic finger interruption detection in electroluminescence images of multicrystalline solar cells, Math. Probl. Eng. 2015, 1 (2015) [CrossRef] [Google Scholar]
  9. H. Chen, H. Zhao, D. Han, K. Liu, Accurate and robust crack detection using steerable evidence filtering in electroluminescence images of solar cells, Opt. Lasers Eng. 118, 22 (2019) [CrossRef] [Google Scholar]
  10. D.-M. Tsai, S.-C. Wu, W.-C. Li, Defect detection of solar cells in electroluminescence images using Fourier image reconstruction, Solar Energy Mater. Solar Cells 99, 250 (2012) [CrossRef] [Google Scholar]
  11. S. Deitsch et al., Automatic classification of defective photovoltaic module cells in electroluminescence images, Solar Energy 185, 455 (2018) [Google Scholar]
  12. X. Li, Q. Yang, Z. Lou, W. Yan, Deep learning based module defect analysis for large-scale photovoltaic farms, IEEE Trans. Energy Convers. 34, 520 (2019) [CrossRef] [Google Scholar]
  13. W. Tang, Q. Yang, K. Xiong, W. Yan, Deep learning based automatic defect identification of photovoltaic module using electroluminescence images, Solar Energy 201, 453 (2020) [CrossRef] [Google Scholar]
  14. M.W. Akram et al., CNN based automatic detection of photovoltaic cell defects in electroluminescence images, Energy 189, 116319 (2019) [CrossRef] [Google Scholar]
  15. L. Liu, Y. Zhu, M.R. Ur Rahman, P. Zhao, H. Chen, Surface defect detection of solar cells based on feature pyramid network and GA-faster-RCNN, in 2019 2nd China Symposium on Cognitive Computing and Hybrid Intelligence (CCHI) (2019), pp. 292–297 [CrossRef] [Google Scholar]
  16. X. Zhang, Y. Hao, H. Shangguan, P. Zhang, A. Wang, Detection of surface defects on solar cells by fusing multi-channel convolution neural networks, Infrared Phys. Technol. 108, 103334 (2020) [CrossRef] [Google Scholar]
  17. U. Otamendi, I. Martinez, M. Quartulli, I.G. Olaizola, E. Viles, W. Cambarau, Segmentation of cell-level anomalies in electroluminescence images of photovoltaic modules, Solar Energy 220, 914 (2021) [CrossRef] [Google Scholar]
  18. J. Balzategui, Eciolaza, D. Maestro-Watson, Anomaly detection and automatic labeling for solar cell quality inspection based on generative adversarial network, Sensor 21, 1 (2021) [Google Scholar]
  19. P. Kunze, S. Rein, M. Hemsemdorf, K. Ramspeck, M. Demant, Learning an empirical digital twin from measurement images for a comprehensive quality inspection of solar cells, Solar RRL 6, 2100482 (2022) [Google Scholar]
  20. J. Redmon, S. Divvala, R. Girshick, A. Farhadi, You only look once: unified, real-time object detection, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2016) [Google Scholar]
  21. A. Bochkovskiy, C.-Y. Wang, H.-Y.M. Liao, YOLOv4: Optimal Speed and Accuracy of Object Detection (2020) [Google Scholar]
  22. C.Y. Wang, A. Bochkovskiy, H.Y.M. Liao, Scaled-yolov4: scaling cross stage partial network, in 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, 2021), doi:10.1109/CVPR46437.2021.01283 [Google Scholar]
  23. K. He, X. Zhang, S. Ren, J. Sun, Spatial pyramid pooling in deep convolutional networks for visual recognition, IEEE Trans. Pattern Anal. Mach. Intell. 37, 1904 (2015) [CrossRef] [PubMed] [Google Scholar]

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