Ensemble deep learning for PV cell defect detection

Researchers have tested eight stand-alone deep learning methods for PV cell fault detection and have found that their accuracy was as high as 73%. All methods were trained and tested on the ELPV benchmark dataset, which includes 2,624 electroluminescence (EL) images of PV cells. Scientists from Saudi Arabia's King Fahd University of Petroleum & Minerals have analyzed the benefits of an ensemble-based deep learning framework for PV cell defects classification. Ensemble deep learning combines multiple deep learning models to improve the accuracy of the prediction. The group tested eight advanced ...

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