Localization of defects in solar cells using luminescence images and deep learning
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[1] A. Sowmya,et al. Half and full solar cell efficiency binning by deep learning on electroluminescence images , 2021, Progress in Photovoltaics: Research and Applications.
[2] S. Wendlandt,et al. Solar Cell Cracks and Finger Failure Detection Using Statistical Parameters of Electroluminescence Images and Machine Learning , 2020, Applied Sciences.
[3] Kun Liu,et al. Deep Learning-Based Solar-Cell Manufacturing Defect Detection With Complementary Attention Network , 2020, IEEE Transactions on Industrial Informatics.
[4] Dezso Sera,et al. Drone-Based Daylight Electroluminescence Imaging of PV Modules , 2020, IEEE Journal of Photovoltaics.
[5] Peng Zhao,et al. Surface Defect Detection of Solar Cells Based on Feature Pyramid Network and GA-Faster-RCNN , 2019, 2019 2nd China Symposium on Cognitive Computing and Hybrid Intelligence (CCHI).
[6] Christian Camus,et al. High-throughput, outdoor characterization of photovoltaic modules by moving electroluminescence measurements , 2019, Optical Engineering.
[7] Shuying Yang,et al. Automated Pipeline for Photovoltaic Module Electroluminescence Image Processing and Degradation Feature Classification , 2019, IEEE Journal of Photovoltaics.
[8] Kun Liu,et al. Classification of Manufacturing Defects in Multicrystalline Solar Cells With Novel Feature Descriptor , 2019, IEEE Transactions on Instrumentation and Measurement.
[9] Luca Antiga,et al. Automatic differentiation in PyTorch , 2017 .
[10] Forrest N. Iandola,et al. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size , 2016, ArXiv.
[11] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[12] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[13] Michael S. Bernstein,et al. ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.
[14] M. Abdullah,et al. Micro-crack detection of multicrystalline solar cells featuring an improved anisotropic diffusion filter and image segmentation technique , 2014, EURASIP J. Image Video Process..
[15] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[16] D. Tsai,et al. Defect detection of solar cells in electroluminescence images using Fourier image reconstruction , 2012 .
[17] M. Köntges,et al. The risk of power loss in crystalline silicon based photovoltaic modules due to micro-cracks , 2011 .
[18] Thorsten Trupke,et al. On the detection of shunts in silicon solar cells by photo‐ and electroluminescence imaging , 2008 .
[19] Hayato Kondo,et al. Analytic findings in the electroluminescence characterization of crystalline silicon solar cells , 2007 .
[20] M. Schubert,et al. Photoluminescence imaging of silicon wafers , 2006 .
[21] Tim Welschehold,et al. Microcracks in Silicon Wafers II: Implications on Solar Cell Characteristics, Statistics and Physical Origin , 2016, IEEE Journal of Photovoltaics.
[22] Mahmoud Abdelhamid,et al. Review of Microcrack Detection Techniques for Silicon Solar Cells , 2014, IEEE Journal of Photovoltaics.
[23] Uwe Rau,et al. Reciprocity relation between photovoltaic quantum efficiency and electroluminescent emission of solar cells , 2007 .