Comparative Detection of Photovoltaic Panel Faults Using U-Net And Yolov8 Models Based on Thermal Images


Uyar M., Canlı E.

12th International New York Conference Onevolving Trends in Interdisciplinary Research & Practices, New York, Amerika Birleşik Devletleri, 21 - 24 Ağustos 2025, ss.336-348, (Tam Metin Bildiri)

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Basıldığı Şehir: New York
  • Basıldığı Ülke: Amerika Birleşik Devletleri
  • Sayfa Sayıları: ss.336-348
  • Bursa Uludağ Üniversitesi Adresli: Evet

Özet

Accurate and timely fault detection in photovoltaic (PV) panels is critical to maintaining operational reliability and energy efficiency. In this study, two deep learning-based approaches are comparatively evaluated for hotspot anomaly detection using thermal images: a classical U-Net-based segmentation model and a YOLOv8-based object detection model. Both models are trained and tested on a real-world thermal image dataset acquired from the Roboflow platform under identical preprocessing conditions. The U-Net model, supported by the segment anything model (SAM) for mask generation, demonstrated strong segmentation capabilities with an average F1-score of 0.8611 and a maximum F1-score of 0.9787, along with an average IoU of 0.7624 and a maximum IoU of 0.9582. On the other hand, the YOLOv8 model outperformed in detection efficiency, achieving a mean average precision (mAP@0.5) of 0.8643, peaking at 0.9283, and exhibiting a faster training curve. The comparative analysis highlights that while U-Net excels in fine-grained pixel-level segmentation, YOLOv8 is more suited for real-time deployment scenarios. The findings underscore the complementary strengths of the two approaches: U-Net is better suited for detailed, pixel-level segmentation tasks, while YOLOv8 offers faster and more efficient detection suitable for real-time deployment. This comparative analysis provides practical insights into model selection strategies for PV fault monitoring systems, based on application-specific requirements.