Deep Learning for Solar Panels Defect Classification Using Data Augmentation Strategies

Abstract

The inspection and maintenance of solar panels face significant challenges due to the dangers involved in checking for potential defects in photovoltaic panels. However, the advancement of studies to facilitate this verification is limited by the lack of image datasets for training algorithms that can perform this task. Based on this principle, the use of Convolutional Neural Networks (CNNs) for training and Data Augmentation for creating artificial data from real images is common. With that said, this work aims to explore configurations and models of Data Augmentation for the classification of defects in solar panels using CNNs. The proposed methodology consists of four experimental stages, where the application of Zoom, rotation, horizontal displacement, and vertical displacement transformations are evaluated, followed by filtering the best results and combining them to find the ideal classification model. The performance of the model proved to be most effective when using a 35-degree rotation for creating artificial images, thus achieving an 88% F1-Score and 87.64% accuracy.