Anomaly detection in IR images of PV modules using supervised contrastive learning-Reference-Cited by-同舟云学术

Anomaly detection in IR images of PV modules using supervised contrastive learning

Published:2022-03-28 Issue:6 Volume:30 Page:597-614
ISSN:1062-7995
Container-title:Progress in Photovoltaics: Research and Applications
language:en
Short-container-title:Progress in Photovoltaics

Author:

Bommes Lukas¹^ORCID,Hoffmann Mathis²³^ORCID,Buerhop‐Lutz Claudia¹^ORCID,Pickel Tobias¹,Hauch Jens¹,Brabec Christoph¹³,Maier Andreas²,Marius Peters Ian¹^ORCID

Affiliation:

1. Forschungszentrum Jülich GmbH Helmholtz‐Institute Erlangen‐Nuremberg for Renewable Energies (HI ERN) Erlangen Germany

2. Pattern Recognition Lab, Department Informatik Universität Erlangen‐Nürnberg (FAU) Erlangen Germany

3. Institute Materials for Electronics and Energy Technology Universität Erlangen‐Nürnberg (FAU) Erlangen Germany

Funder

Bayerisches Staatsministerium für Ernährung, Landwirtschaft und Forsten

Publisher

Wiley

Subject

Electrical and Electronic Engineering,Condensed Matter Physics,Renewable Energy, Sustainability and the Environment,Electronic, Optical and Magnetic Materials

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/pip.3518

Reference80 articles.

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