Accurate detection of photovoltaic panel defects via visible-infrared image fusion and a lightweight enhanced YOLOv11n model

Abstract Photovoltaic panels are susceptible to environmental conditions and manufacturing defects during operation. Timely automated detection is crucial for maintaining power generation efficiency and ensuring equipment safety. This paper presents a lightweight enhanced YOLOv11n model for detecting photovoltaic panel defects through visible–infrared image fusion. The proposed model integrates a cross-modulation fusion network that extracts structural texture features from visible images and thermal anomaly features from infrared images, enabling effective cross-modal fusion without explicit geometric registration. Moreover, to enhance both detection accuracy and adaptability for edge deployment, the model incorporates a Semantic-Aware Downsampling Module (NAMDown), a Lightweight Feature Extraction Module (LFEM), and a Location Quality Estimation Head (LQE-Head). Furthermore, the InterpIoU loss function, tailored for small targets, is employed to enhance regression performance. Experiments are conducted on a self-constructed dual-modal dataset for photovoltaic panel defects. The results indicate that the proposed model achieves 81.33% accuracy and surpasses existing lightweight detection methods in both accuracy and recall. Finally, the model is deployed on the Jetson AGX Orin edge platform. With TensorRT optimization, the average inference delay is reduced to 29.4 ms, demonstrating a favorable balance between accuracy and real-time performance.