Deep Learning Based Detection of Cracks in Electroluminescence Images of Fielded PV modules-Reference-Cited by-同舟云学术

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Deep Learning Based Detection of Cracks in Electroluminescence Images of Fielded PV modules

Published:2020-06-14 Issue: Volume: Page:
ISSN:
Container-title:2020 47th IEEE Photovoltaic Specialists Conference (PVSC)
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Author:

Chindarkkar Amey,Priyadarshi Sweta,Shiradkar Narendra S.,Kottantharayil Anil,Velmurugan Rajbabu

Funder

National Centre for Photovoltaic Research and Education (NCPRE)

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9300349/9300350/09300615.pdf?arnumber=9300615

Cited by 8 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Shingle cell IV$$ IV $$ characterization based on spatially resolved host cell measurements;Progress in Photovoltaics: Research and Applications;2023-12-26

2. Photovoltaic (PV) Module Defect Image Classification Analysis Using EfficientNetV2 Architectures;2023 IEEE 14th Control and System Graduate Research Colloquium (ICSGRC);2023-08-05

3. Convolution Neural Network (CNN) Architectures Analysis for Photovoltaic (PV) Module Defect Images Classification;2022 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM);2022-11-22

4. PSOPruner: PSO-Based Deep Convolutional Neural Network Pruning Method for PV Module Defects Classification;IEEE Journal of Photovoltaics;2022-11

5. Contactless Inline IV Measurement of Solar Cells Using an Empirical Model;Solar RRL;2022-08-07

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