红外光伏组件缺陷检测技术:融合跨尺度特征的改进YOLO11-EICM方法OA

Infrared detection of photovoltaic module defects is crucial for ensuring the safe operation of photovoltaic power stations and improving maintenance efficiency.However,existing infrared thermal imaging detection methods face significant challenges,such as low contrast,blurred edges,and drastic variations in defect scales,which lead to insufficient edge feature extraction,inefficient cross-scale feature fusion,and high false negative rates for small-scale defects.To address these issues,this paper proposes an improved YOLO11-EICM based infrared detection algorithm for photovoltaic module defects to diagnose module faults.Extensive experiments on an infrared photovoltaic module defect dataset demonstrate that the proposed algorithm achieves a mAP@0.5 of 83.8%and a recall rate of 79.4%,representing improvements of 3.46%and 8.03%respectively over the existing YOLO11n model,while reducing the parameter count by 8.91%to just 2.35 million.