Contrastive learning for solar cell micro-crack detection

As the core component of the photovoltaic system, the quality of solar cells determines the conversion efficiency of electric energy. Some strategies have been proposed to detect the crack of solar cells, but most of them can not detect the crack efficiently. This paper proposed a new two-stage method for microcrack detection in polycrystalline images based on contrastive learning. First, the input picture without a label is learned by SimCLR to obtain the representation of the image. In the second stage, the linear classifier is trained based on the fixed encoder and the representation. In the comparative experiment, unsupervised contrastive learning is compared with cross-entropy training and supervised contrastive learning. The experimental results show that the linear classifier trained on unsupervised representation achieves a top-1 accuracy of 78.39%, which is 7.42% higher than the supervised contrastive learning method, compared with supervised learning, the results are comparable.

[1]  Christian Riess,et al.  Segmentation of Photovoltaic Module Cells in Electroluminescence Images , 2018, ArXiv.

[2]  Oriol Vinyals,et al.  Representation Learning with Contrastive Predictive Coding , 2018, ArXiv.

[3]  Mohd Abdullah,et al.  Micro-crack detection of multicrystalline solar cells featuring an improved anisotropic diffusion filter and image segmentation technique , 2014, EURASIP Journal on Image and Video Processing.

[4]  Geoffrey E. Hinton,et al.  A Simple Framework for Contrastive Learning of Visual Representations , 2020, ICML.

[5]  M. Pellegrino,et al.  Analysis Techniques for Photovoltaic Modules Based on Amorphous Solar Cells , 2017 .

[6]  Yuanyuan Wu,et al.  Micro-cracks detection of multicrystalline solar cell surface based on self-learning features and low-rank matrix recovery , 2018, Sensor Review.

[7]  Christoph J. Brabec,et al.  A Benchmark for Visual Identification of Defective Solar Cells in Electroluminescence Imagery , 2018 .

[8]  Yann LeCun,et al.  Dimensionality Reduction by Learning an Invariant Mapping , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[9]  Mert R. Sabuncu,et al.  Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels , 2018, NeurIPS.

[10]  Mauricio Marengoni,et al.  A Survey of Transfer Learning for Convolutional Neural Networks , 2019, 2019 32nd SIBGRAPI Conference on Graphics, Patterns and Images Tutorials (SIBGRAPI-T).

[11]  Raymond R. Tan,et al.  An improved moth-flame optimization algorithm for support vector machine prediction of photovoltaic power generation , 2020 .

[12]  Jing Li,et al.  Micro-crack detection of solar cell based on adaptive deep features and visual saliency , 2020 .

[13]  Jiang Yang-yu.  Research on the Impact of Hidden Crack on PV Modules , 2012 .

[14]  Thomas Brox,et al.  Discriminative Unsupervised Feature Learning with Convolutional Neural Networks , 2014, NIPS.

[15]  Ce Liu,et al.  Supervised Contrastive Learning , 2020, NeurIPS.

[16]  Kanjian Zhang,et al.  Micro-cracks Detection of Polycrystalline Solar Cells with Transfer Learning , 2020 .

[17]  Christian Riess,et al.  Automatic Classification of Defective Photovoltaic Module Cells in Electroluminescence Images , 2018, Solar Energy.

[18]  Thomas Brox,et al.  Microcracks in Silicon Wafers I: Inline Detection and Implications of Crack Morphology on Wafer Strength , 2016, IEEE Journal of Photovoltaics.