The reliable operation of photovoltaic (PV) systems is essential for sustainable energy production, yet their efficiency is often compromised by defects such as bird droppings, cracks, and dust accumulation. Automated defect detection is critical for addressing these challenges in large-scale solar farms, where manual inspections are impractical. This study evaluates three YOLO object detection models—YOLOv5, YOLOv8, and YOLOv11—on a comprehensive dataset to identify solar panel defects. YOLOv5 achieved the fastest inference time (7.1 ms per image) and high precision (94.1%) for cracked panels. YOLOv8 excelled in recall for rare defects, such as bird drops (79.2%), while YOLOv11 delivered the highest mAP@0.5 (93.4%), demonstrating a balanced performance across the defect categories. Despite the strong performance for common defects like dusty panels (mAP@0.5 > 98%), bird drop detection posed challenges due to dataset imbalances. These results highlight the trade-offs between accuracy and computational efficiency, providing actionable insights for deploying automated defect detection systems to enhance PV system reliability and scalability.

The reliable operation of photovoltaic (PV) systems is essential for sustainable energy production, yet their efficiency is often compromised by defects such as bird droppings, cracks, and dust accumulation. Automated defect detection is critical for addressing these challenges in large-scale solar farms, where manual inspections are impractical. This study evaluates three YOLO object detection models—YOLOv5, YOLOv8, and YOLOv11—on a comprehensive dataset to identify solar panel defects. YOLOv5 achieved the fastest inference time (7.1ms per image) and high precision (94.1%) for cracked panels. YOLOv8 excelled in recall for rare defects, such as bird drops (79.2%), while YOLOv11 delivered the highest mAP@0.5 (93.4%), demonstrating balanced performance across defect categories. Despite strong performance for common defects like dusty panels (mAP@0.5 > 98%), bird drop detection posed challenges due to dataset imbalances. These results highlight the trade-offs between accuracy and computational efficiency, providing actionable insights for deploying automated defect detection systems to enhance PV system reliability and scalability.