Photovoltaic Anomaly Detection in Electroluminescence Images Using a Transformer-Enhanced YOLO Algorithm

Abstract

Accurate and efficient detection of defects during the manufacturing process of photovoltaic (PV) panels is critical for ensuring product quality and operational reliability. However, developing lightweight deep learning models capable of handling diverse defect types remains a significant challenge. To address this issue, this study introduces a comprehensive 10-class electroluminescence (EL) dataset to the literature, which includes the Striation Rings class, a defect type rarely represented in publicly available datasets. To improve the representation of underrepresented defect categories, Stable Diffusion is employed for controlled synthetic data augmentation. Furthermore, this study proposes a lightweight and enhanced You Only Look Once (YOLO)11-based architecture specifically designed for superior defect detection. The proposed model integrates a Spatial Gated C3 Transformer module and a Fusion module into the base YOLO11s architecture to enhance feature extraction and better capture critical spatial information. Additionally, Wise-IoU is used as the loss function to optimize bounding box regression. In order to assess its effectiveness, the proposed model is evaluated against a comprehensive suite of state-of-the-art object detection frameworks, encompassing successive YOLO generations, Faster R-CNN, and RF-DETR. Experimental evaluations reveal that the proposed model surpasses all competing methods by a substantial margin. The model attains an mAP@0.5 score of 0.9876, yielding an 8.81% improvement over the baseline YOLO11s architecture. In addition, extensive cross-dataset experiments conducted on the PVEL-AD dataset further validate the model’s generalization capability. The results consistently demonstrate strong defect detection performance with substantially lower computational complexity, highlighting the effectiveness, robustness, and reliability of the proposed framework for real-world photovoltaic defect inspection applications.