A Comparison of CNN, Transformer, and Open-Vocabulary Architectures for Real-Time Photovoltaic Defect Detection Using UAV Thermal Imagery

Real-time defect detection in solar farms is critical for profitability and safety. This paper compares state-of-the-art (SOTA) object detection architectures for deployment on edge computing platforms for the purpose of thermal PV defect detection using UAV imagery. We systematically evaluated Closed-Set (YOLOv10, YOLOv12, RT-DETR, RF-DETR) and Open-Vocabulary (YOLO-World, OWL-ViT) models on a public thermal dataset. Our results highlight two leading candidates. The transformer-based RF-DETR sets a new accuracy record at 82.6% mAP@0.50, driven by its self-supervised backbone, but its inference speed is low (12.6 FPS). Conversely, the CNN-based YOLO-World integrates language semantics to reach a competitive 78.1% mAP@0.50 while operating at a real-time speed of 31.3 FPS. We conclude that both RF-DETR and YOLO-World are promising for embedded thermal fault detection. The final selection will depend on on-platform inference performance.