Solar Panel Thermal Images

Our study provides fault detection of solar panels using resource-efficient UAVs (Unmanned Air Vehicle) combined with image processing and deep learning methods to facilitate the maintenance and repair of the solar panels. Our method provide technical details of faulty panels along with their specific fault types on an individual panel basis which facilitates quick and precise identification of faulty panels. We compared the classification performance of several deep learning and visual transformer models on our novel data set in terms of accuracy, precision, recall and f1-score metrics. The Swin Transformer model outperformed all other models on the test data with 0.8381 accuracy, 0.8383 precision, 0.8381 recall and 0.8368 f1-score