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.