针对光伏（PV）电池缺陷检测中存在的数据不均衡、缺陷尺度不一和背景纹理复杂多变等因素导致的误检、漏检问题，提出一种基于YOLOv8的缺陷检测算法YOLOv8-EL。首先，使用GauGAN进行数据增强，处理数据集的类内和类间不均衡的问题，提高模型泛化能力，降低模型过拟合的风险；其次，在主干网络和特征融合网络之间嵌入上下文聚合模块，自适应地融合不同层次的语义信息，对齐局部特征，减少微小微弱缺陷信息的丢失，抑制无关背景信息的干扰；最后，构建多注意力检测头以替换解耦头，引入不同的注意力机制细化分类和定位任务，提取空间和通道层面上的关键信息，减少特征混淆。实验结果表明，该模型在扩充后的PV电池EL数据集上的平均精度达到89.90%，模型参数量为13.13×10⁶，在提升精度的基础上兼顾了后续部署时轻量化的要求。在PASCAL VOC数据集上进行泛化性实验，证明了改进算法的泛化性能。

A YOLOv8-based defect detection algorithm, YOLOv8-EL, is proposed to address the problems of false detection and missing detection caused by data imbalance, varied defect scales, and complex background textures in photovoltaic (PV) cell defect detection. First, GauGAN is used for data augmentation to address the issue of intra-class and inter-class imbalance, improve model generalization ability, and reduce the risk of overfitting. Second, a context aggregation module is embedded between the backbone network and the feature fusion network to adaptively fuse semantic information from different levels, align local features, reduce the loss of minor defect information, and suppress irrelevant background interference. Finally, a multi-attention detection head is constructed to replace the decoupling head, introducing different attention mechanisms to refine classification and localization tasks, extract key information at the spatial and channel levels, and reduce feature confusion. Experimental results show that the proposed model achieves an average precision of 89.90% on the expanded PV cell EL dataset with a parameter count of 13.13×10⁶, achieving both precision improvement and lightweight deployment requirements. Generalization experiments on the PASCAL VOC dataset demonstrate the improved algorithm's generalization performance.