现有光伏组件缺陷识别方法存在提取特征困难、实时性较差导致了对光伏组件的缺陷故障检测的识别精度不高，本文提出一种基于改进 YOLO v5算法的光伏组件红外热成像缺陷检测方法。改进后的 YOLO v5算法主要是在原来的基础上增添注意机制 SE模块，并且改进损失函数将 GIoU改为 EIoU提高模型收敛效果、最后采用 KG模块平衡特征金字塔结构对模型进行优化，用以提高 YOLOv5算法的识别精度和收敛效果。改进后的网络结构应用在 YOLO v5s模型中，在光伏组件红外图像的检测上的平均检测精度 mAP可以达到 92.8%，比原本的 YOLO v5s算法 88.3%提升了 4.5%，在精确度和召回率上的收敛效果也比原始 YOLO v5算法模型有所提高，改进后的网络结构应用于 l、m、x三种模型中，其检测精度都有所提升，因此改进后的 YOLOv5算法适用于 4种模型。

To solve the problem of difficulty in extracting features and poor real-time performance of existing photovoltaic power station defect identification methods, which lead to low identification accuracy of photovoltaic module defect detection, this paper proposes a photovoltaic power station infrared thermal imaging defect detection method based on an improved YOLO v5 algorithm. The improved YOLO v5 algorithm primarily adds an attention mechanism SE module to the original core and improves the loss function from GIoU to EIoU to enhance the model convergence effect. Finally, the knowledge graph (KG) module is used to balance the feature pyramid structure and optimize the model to improve the YOLO v5 algorithm’s recognition accuracy and convergence effects. The improved network structure was applied to the YOLO v5s model, whereby the average detection accuracy mAP used in the detection of infrared images of photovoltaic power plants reached 92.8%, which is 4.5% higher than that of the original YOLO v5s algorithm (88.3%). The effect of convergence on the precision and recall rate was also improved compared with the original YOLO v5 algorithm model. By applying the enhanced network structure to the three models (l, m, and x), detection accuracy was also improved. Consequently, the improved YOLO v5 algorithm is suitable for the four models.