随着光伏发电技术的快速发展，光伏组件缺陷检测对电站运维效率及能源安全至关重要，然而传统的检测方法在处理航拍图像时易受多尺度特征模糊与微小目标漏检的局限，难以满足高精度、实时化的工程需求。针对上述问题，提出了一种基于YOLOv8 （you only look once v8）的优化模型RD （recursive depthwise）-YOLO，该模型通过引入多尺度通道挤压激励模块（multi-scale channel squeeze-and-excitation network, MultiSENet），增强了对多尺度特征的捕捉能力；同时结合递归深度可分离卷积注意力模块（recursive depthwise separable convolutional block attention module, RDCBAM）优化通道信息，提升模型对于小目标的检测精度。通过自制数据集进行实验测试，结果表明，RD-YOLO相较于传统检测模型，精度和效率均有显著提升。

With the rapid development of photovoltaic (PV) power generation technology, defect detection in PV modules is crucial for the operational efficiency and energy security of power plants. However, traditional detection methods are often limited when processing aerial images, as they are prone to issues such as multi-scale feature blurring and small target missed detection, making it difficult to meet the high precision and real-time engineering requirements. To address these issues, an optimized model based on YOLOv8(you only look once v8), named RD (recursive depthwise)-YOLO, was proposed. A multi-scale channel squeeze-and-excitation network (MultiSENet) was introduced to enhance its ability to capture multi-scale features. Additionally, a recursive depthwise separable convolutional block attention module (RDCBAM) was incorporated to optimize channel information and improve the model's detection accuracy for small targets. Experiments were conducted using a self-constructed dataset, and the results indicate that RD-YOLO significantly improves both the accuracy and efficiency compared to traditional detection models.