RFE-YOLO: A Study on Photovoltaic Module Fault Detection Algorithm Based on Multimodal Feature Fusion

The operational status of photovoltaic modules directly impacts power generation efficiency, making rapid and precise fault detection crucial for intelligent operation and maintenance of Photovoltaic (PV) power plants. Addressing the perceptual limitations of single-modal images in complex environments, this study constructs an RGBIRPV multimodal dataset tailored for centralized PV power plants and proposes an RFE-YOLO model. This model enhances detection performance through three core mechanisms: The RC module employs a CBAM-based attention mechanism for multi-parameter feature extraction, utilizing heterogeneous RC_V and RC_I architectures to achieve differentiated feature enhancement for visible and infrared modalities. The lightweight adaptive fusion FA module introduces learnable modality balance and attention cascading mechanisms to optimize multimodal information fusion. Concurrently, the multi-scale enhanced EVG module based on GSConv achieves synergistic representation of shallow details and deep semantics with low computational overhead. The experiment employed an 8:1:1 data partitioning scheme. Compared to the YOLOv11n model employing feature-level mid-fusion, the model proposed in this study achieves improvements of 2.9%, 1.8%, and 1.5% in precision, mAP@50, and F1 score, respectively. It effectively meets the demand for rapid and accurate detection of PV module failures in real power plant environments, providing an effective technical solution for intelligent operation and maintenance of photovoltaic power plants.