Solar Panel Anomaly Detection Dataset Based on Solar Insecticidal Lamp Internet of Things

The rapid growth of solar energy infrastructure necessitates efficient and automated inspection methods to maintain the performance and longevity of photovoltaic (PV) panels. Traditional manual inspection is time-consuming, labor-intensive, and prone to human error. In this study, we propose a computer vision-based approach for the automated detection of surface anomalies on solar panels using a YOLO object detection model. Our method focuses on identifying common physical defects\u2014including physical cracks, bird droppings, and dust accumulation\u2014from standard RGB images. To support model development and evaluation, we introduce a publicly available annotated image dataset containing diverse examples of solar panel anomalies under various environmental and lighting conditions.