Solar-cell panels use sunlight as jerome brown jersey a source of energy to generate electricity.However, the performances of solar panels decline when they degrade, owing to defects.Some common defects in solar-cell panels include hot spots, cracking, and dust.Hence, it is important to efficiently detect defects in solar-cell panels and repair them.In this study, we propose a lightweight inception residual convolutional network (LIRNet) to detect defects in solar-cell panels.
LIRNet is a neural network model that utilizes deep learning techniques.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.The first phase is the data-preprocessing stage.We use the K-means clustering algorithm to refine the dataset.The second phase is the training of the model.
We designed a powerful and lightweight neural network model to enhance accuracy and speed up the training ivoryjinelle.com time.In the experiment, LIRNet improved the accuracy by approximately 8% and performed ten times faster than EfficientNet.