Solar Panel

Solar panel research currently focuses on improving automated detection and analysis using aerial imagery, driven by the need for efficient large-scale monitoring and maintenance of solar energy infrastructure. This involves developing advanced computer vision models, such as transformers and object detectors (e.g., YOLOv3), often incorporating self-supervised learning techniques to enhance accuracy and robustness across varying conditions. These advancements enable precise localization, size estimation, and even defect detection (e.g., hotspots, soiling) in solar panels, leading to optimized energy production and reduced maintenance costs. The resulting data contributes significantly to energy policy and the expansion of sustainable energy resources.