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Efficient Cell Segmentation from Electroluminescent Images of Single-Crystalline Silicon Photovoltaic Modules and Cell-Based Defect Identification Using Deep Learning with Pseudo-Colorization

机译：利用伪色多化的深度学习，从单晶硅光伏模块的电致发光图像和基于细胞的缺陷识别的有效细胞分割

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Horng-Horng Lin; Harshad Kumar Dandage; Keh-Moh Lin; You-Teh Lin; Yeou-Jiunn Chen;
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年度 2021
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1. Deep learning based automatic defect identification of photovoltaic module using electroluminescence images [J] . Solar Energy . 2020,第May期

机译：基于深度学习的电致发光图像自动识别光伏组件缺陷
2. Automatic detection of photovoltaic module defects in infrared images with isolated and develop-model transfer deep learning [J] . Akram M. Waqar, Li Guiqiang, Jin Yi, Solar Energy . 2020,第Mara期

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3. Efficient deep feature extraction and classification for identifying defective photovoltaic module cells in Electroluminescence images [J] . Demirci Mustafa Yusuf, Besli Nurettin, Gumuscu Abduelkadir Expert systems with applications . 2021,第Auga期

机译：有效的深度特征提取和分类，用于识别电致发光图像中的缺陷光伏模块电池
4. PV-AIDED: Photovoltaic Artificial Intelligence Defect Identification. Multichannel encoder-decoder ensemble models for electroluminescence images of thin-film photovoltaic modules, PEARL TF-PV. [C] . Evgenii Sovetkin, Bart Pieters, Thomas Weber, European Photovoltaic Solar Energy Conference and Exhibition . 2021

机译：PV辅助：光伏人工智能缺陷识别。薄膜光伏模块的电致发光图像的多通道编码器 - 解码器型号，珍珠TF-PV。
5. Nuclei segmentation of histology images based on deep learning and color quantization and analysis of real world pill images [D] . Sornapudi, Sudhir. 2017

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6. Deep longitudinal transfer learning-based automatic segmentation of photoreceptor ellipsoid zone defects on optical coherence tomography images of macular telangiectasia type 2 [O] . Jessica Loo, Leyuan Fang, David Cunefare, 2018

机译：基于深度纵向转移学习的黄斑性毛细血管扩张2型光学相干断层扫描图像上的感光体椭球区域缺陷的自动分割
7. Intelligent Defect Detection Method of Photovoltaic Modules Based on Deep Learning [O] . Binbin Ni, Pingguo Zou, Qiang Li, 2018

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Efficient Cell Segmentation from Electroluminescent Images of Single-Crystalline Silicon Photovoltaic Modules and Cell-Based Defect Identification Using Deep Learning with Pseudo-Colorization

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