Enhanced Classification of Damaged Solar Cells Using Transfer Learning with Pre-Trained Networks
Abstract
Among renewable resources, solar power is the most necessary one. Unfortunately, the performance of solar panels depends to a great extent on their components, particularly damagedsolar cells, which can also result in possible failure of the system. Early detection of these defects is important in order to maintain solar energy systems efficiently. Electroluminescence imaging has emerged as a powerful technique for quickly identifying defects in photovoltaic cells, and new developments in deep learning have enabled the automation of this diagnostic process. In this work, we implemented transfer learning on three popular convolutional neural networks: ResNet50, VGG16, and InceptionV3. They were to be classified based on images taken from electroluminescence, the goal of the I hold talons. We relied on a publicly.
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