目的 在光伏电池片制作流程中，由于正负极电路的丝印缺陷具有目标小、分布不均匀等特征，目前检测仍然耗时耗力，因此提出基于优化YOLOv8算法的丝印缺陷检测方法，以解决光伏丝印电极缺陷难检测的问题。方法 基于机器视觉理论搭建电池片图像采集平台，采集图像并对图像数据集进行标注划分，在批量变换处理增强数据集模型基础上进行YOLOv系列算法比对实验，表明YOLOv8算法更适合局部小目标缺陷的检测。接着将深度学习技术的洗牌注意力机制(Shuffle Attention，SA)引入YOLOv8算法中的注意力模块，有效提取特征信息，替换原特征融合模块，最后与原算法模型进行消融实验。结果 缺陷识别精度提升了4.6百分点。结论 优化后的算法能够提高缺陷识别精度，有效降低丝印产生的不良电池片进入后续工业流程的概率。

In the photovoltaic cell production process, due to the positive and negative circuit screen printing defects with small target, uneven distribution and other characteristics, the current detection is still time-consuming and labor-intensive. The work aims to propose a screen printing defect detection method based on the YOLOv8 optimization algorithm to solve the problem of difficult detection of photovoltaic screen printing electrode defects. Based on the theory of machine vision, a battery cell image acquisition platform was built to collect images, label and divide image data sets, and conduct batch transformation processing to enhance the data set model based on the YOLOv series of algorithm comparison experiments, indicating that the YOLOv8 algorithm was more suitable for the detection of defects in small localized targets. Then, the shuffle attention mechanism (SA) of deep learning technology was introduced into the attention module of the YOLOv8 algorithm to effectively extract feature information, replace the original feature fusion module, and finally conduct ablation experiments with the original algorithm model. The results showed that the defect recognition accuracy was improved by 4.6 percentage points. The optimized algorithm can improve the defect recognition accuracy and effectively inhibit the chances of defective cells generated by screen printing entering the subsequent industrial process.