New deep learning tech uses electroluminescence images to identify defective PV cells

The novel method uses the YOLOv8 framework, integrating an attention mechanism and a transformer model. It was tested on a dataset of 4,500 electroluminescence images against several other models and its results were up to 17. 2% more accurate. Scientists from China have developed a new deep-learning method for detecting defects in PV cells. Analyzing electroluminescence (EL) images, the novel system utilizes the YOLOv8 convolutional neural network (CNN) architecture, integrating an attention mechanism and a transformer model. "To further improve the accuracy, I think we can start by optimizing ...

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