Comparative Evaluation of YOLO- and Transformer-Based Models for Photovoltaic Fault Detection Using Thermal Imagery

Photovoltaic systems represent one of the most reliable and widely used technologies for electricity generation from renewable energy sources, although their performance is affected by the occurrence of faults and defects that lead to energy losses and efficiency reduction. Therefore, detecting and localizing defects in photovoltaic panels is essential. A wide variety of image analysis techniques based on aerial thermal imagery acquired by drones have been widely implemented for proper maintenance operations, requiring a comprehensive comparison among these approaches to assess their relative performance and suitability for different scenarios. This study presents a comparative evaluation of several vision-based approaches using artificial intelligence for photovoltaic defect detection. YOLO- and Transformer-based models are analyzed and benchmarked in terms of accuracy, inference time, per-class performance, and sensitivity to object size. Experimental results demonstrate that both YOLO- and Transformer-based models are computationally lightweight and suitable for real-time implementation. However, Transformer-based architectures exhibit higher detection accuracy and stronger generalization capabilities, while YOLOv5 achieves superior inference speed. The RF-DETR-Small model provides the best balance between accuracy, computational efficiency, and robustness across different defect types and object scales. These findings highlight the potential of Transformer-based vision models as a highly effective alternative for real-time, on-site photovoltaic fault detection and predictive maintenance applications.