Defect detection on Polycrystalline solar cells using Electroluminescence and Fully Convolutional Neural Networks-Reference-Cited by-同舟云学术

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Defect detection on Polycrystalline solar cells using Electroluminescence and Fully Convolutional Neural Networks

Published:2020-01 Issue: Volume: Page:
ISSN:
Container-title:2020 IEEE/SICE International Symposium on System Integration (SII)
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Author:

Balzategui Julen,Eciolaza Luka,Arana-Arexolaleiba Nestor

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9011449/9025808/09026211.pdf?arnumber=9026211

Cited by 28 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

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2. Defect detection of solar cells based on improved YOLOv5s;Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024);2024-06-05

3. CutPaste-ROI: An Industrial Anomaly Data Detection Method based on Self-supervised Learning;Journal of Imaging Science and Technology;2024-03-01

4. Classification of defective product for smart factory through deep learning method;AIP Conference Proceedings;2024

5. Improved Solar Photovoltaic Panel Defect Detection Technology Based on YOLOv5;Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering;2024

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