YOLO-PV: An Enhanced YOLO11n Model with Multi-Scale Feature Fusion for Photovoltaic Panel Defect Detection

Photovoltaic (PV) panel defect detection is essential for maintaining power generation efficiency and ensuring the safe operation of solar plants. Conventional detectors often suffer from low accuracy and limited adaptability to multi-scale defects. To address this issue, we propose YOLO-PV, an enhanced YOLO11n-based model incorporating three novel modules: the Enhanced Hybrid Multi-Scale Block (EHMSB), the Efficient Scale-Specific Attention Block (ESMSAB), and the ESMSAB-FPN for refined multi-scale feature fusion. YOLO-PV is evaluated on the PVEL-AD dataset and compared against representative detectors including YOLOv5n, YOLOv6n, YOLOv8n, YOLO11n, Faster R-CNN, and RT-DETR. Experimental results demonstrate that YOLO-PV achieves a 6.7% increase in Precision, a 2.9% improvement in mAP@0.5, and a 4.4% improvement in mAP@0.5:0.95, while maintaining real-time performance. These results highlight the effectiveness of the proposed modules in enhancing detection accuracy for PV defect inspection, providing a reliable and efficient solution for smart PV maintenance.