A Data-Efficient Approach to Solar Panel Micro-Crack Detection via Semi-Supervised Learning

This study presents a method for the automatic identification of micro-cracks in photovoltaic solar modules using deep learning techniques. The main challenge in this research is the lack of labeled data and class imbalance for the detection of micro-cracks. The proposed method employs a multi-stage approach. Initially, 10% of the dataset is manually labeled to train a simple convolutional neural network model. This model is then used to generate pseudo-labels for the unlabeled data using a semi-supervised approach. The pseudo-labels are manually reviewed to increase the number of micro-crack samples in the training set. Data augmentation techniques are also applied to increase the size and diversity of the training dataset. Finally, the pre-trained ResNet-50 model is fine-tuned on the expanded labeled dataset for accurate detection of micro-cracks. Advanced preprocessing steps, including solar cell segmentation, cropping, and data augmentation, have been performed. The class imbalance problem is addressed through undersampling and weighted loss functions. The experimental results demonstrate the effectiveness of the proposed method, achieving an accuracy of 0.978 and an F1-score of 0.797 in the detection of micro-cracks in electroluminescence images of solar panels. This study provides insights into the use of limited labeled data for training robust deep learning models for the identification of defects in solar modules.