Defect detection of photovoltaic modules based on improved VarifocalNet
Defect detection of photovoltaic modules based on improved VarifocalNet
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Publisher
London: Nature Publishing Group UK
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Language
English
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London: Nature Publishing Group UK
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Contents
Detecting and replacing defective photovoltaic modules is essential as they directly impact power generation efficiency. Many current deep learning-based methods for detecting defects in photovoltaic modules focus solely on either detection speed or accuracy, which limits their practical application. To address this issue, an improved VarifocalNet...
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Full title
Defect detection of photovoltaic modules based on improved VarifocalNet
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TN_cdi_doaj_primary_oai_doaj_org_article_3341233f82144939a26e53025d850718
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_3341233f82144939a26e53025d850718
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ISSN
2045-2322
E-ISSN
2045-2322
DOI
10.1038/s41598-024-66234-3
