We designed a powerful and lightweight neural network model to enhance accuracy and speed up the training time. The second phase is the training of the model. We use the K-means clustering algorithm to refine the dataset. The first phase is the data-preprocessing stage. To achieve high model performance on solar panels, including high fault detection accuracy and processing speed, LIRNet draws on hierarchical learning, which is a two-phase solar-panel-defect classification method. LIRNet is a neural network model that utilizes deep learning techniques. In this study, we propose a lightweight inception residual convolutional network (LIRNet) to detect defects in solar-cell panels. Hence, it is important to efficiently detect defects in solar-cell panels and repair them. Some common defects in solar-cell panels include hot spots, cracking, and dust. However, the performances of solar panels decline when they degrade, owing to defects. Solar-cell panels use sunlight as a source of energy to generate electricity.
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