CNN-based Deep Learning Approach for Micro-crack Detection of Solar Panels

With the growing popularity and decreasing cost of solar power, crystalline solar panels have been widely adopted in residential and commercial applications. Increased production and prolonged usage of photovoltaic (PV) modules necessitate automatic detection of defects in utility-scale solar power plants. Micro-cracks in particular is are a type of defect that degrade the performance of the modules. This study aims to extend the industrial application of image classification by implementing state-of-the-art convolutional neural network (CNN) architectures and an ensemble of CNNs for identifying micro-cracks from electroluminescence (EL) images of PV modules. Transfer learning has become increasingly popular for mitigating the prerequisite of large training datasets and for performing satisfactorily on smaller, more practical datasets. In this study, pre-trained models like…