Lightweight multi-modal feature enhancement network for photovoltaic fault detection

Efficient fault detection in photovoltaic (PV) modules is pivotal for maintaining the operational safety and stability of solar power generation systems. Despite significant advancements in deep learning-based fault detection, existing models often struggle with limited feature extraction and low efficiency, impeding real-time deployment in complex environments. To address these issues, this paper proposes a lightweight and high-precision fault detection model based on YOLOv7-tiny. The proposed model integrates a thermal infrared and temperature information fusion module into the backbone network to enhance multi-modal feature representation. To ensure computational efficiency, GhostConv is utilized to replace standard convolutional layers, minimizing parameter overhead without compromising performance. Furthermore, a cross-scale lightweight attention module is designed to improve the contextual awareness and localization performance for small targets. Prior to the detection head, a position information perception module is introduced to enhance global semantic modeling and local directional sensitivity. Experimental results demonstrate that the model achieves mAP50 and mAP50:95 of 93.5% and 59.4%, respectively, on an infrared photovoltaic defect dataset. Combining a lightweight architecture with improved accuracy and robustness, the proposed model demonstrates significant potential for integration into intelligent photovoltaic operation and maintenance systems.