由于光伏电池片表面缺陷尺度细微、与背景特征高度重叠，传统检测算法频繁出现漏检与误检，现有方法的缺陷识别鲁棒性亟待提升。本文旨在改进YOLOv8n模型，提出SBC-YOLOv8n（SE-BiFPN-CA-YOLOv8n）以提升在复杂工业场景中的识别准确率与稳定性，提升光伏电池片微小缺陷的检测能力。本文从三方面对模型进行优化：一是在主干网络SPPF模块前后融合CA与SE双重注意力机制，分别从空间位置感知和通道权重调整两个维度增强对细微缺陷的特征提取能力；二是采用加权双向特征金字塔网络（BiFPN）替代原颈部网络结构，通过精简节点和双向加权融合机制强化多尺度特征交互，抑制背景干扰；三是针对类别不平衡问题，优化Focal Loss损失函数参数，将聚焦参数γ从2提升至3以加强对难分类样本的关注，并将类别平衡因子α从0.25调整至0.5，显著增加缺陷样本的损失权重、降低背景权重。改进后的SBC-YOLOv8n模型在测试集上达到mAP@0.5为80.2%，较原始YOLOv8n模型提升4.2%；同时，精确率、召回率和F1分数也均有显著提升，在保持模型实时性的前提下，有效提升了对微小缺陷的检出能力与整体鲁棒性。

Due to the fine scale of surface defects on photovoltaic cells and their high degree of overlap with background features, traditional detection algorithms frequently suffer from missed detections and false positives. The robustness of existing methods in complex scenarios needs to be improved. This paper aims to enhance the YOLOv8n model by proposing SBC-YOLOv8n (SE-BiFPN-CA-YOLOv8n)to improve the detection ability of micro-defects on photovoltaic cells and increase the recognition accuracy and stability in complex industrial environments. The model is optimized in three aspects: First, dual attention mechanisms, namely CA and SE, are integrated before and after the SPPF module in the backbone network to enhance the feature extraction ability of micro-defects from both spatial location perception and channel weight adjustment. Second, the BiFPN is adopted to replace the original neck network structure, which simplifies nodes and uses a bidirectional weighted fusion mechanism to strengthen multi-scale feature interaction and suppress background interference. Third, to address the class imbalance issue, the Focal Loss function parameters are optimized. The focusing parameter γ is increased from 2 to 3 to enhance the attention to hard-to-classify samples, and the class balance factor α is adjusted from 0.25 to 0.5, significantly increasing the loss weight of defect samples and reducing the background weight. The improved SBC-YOLOv8n model achieves an mAP@0.5 of 80.2% on the test set, a 4.2 percentage point increase over the original YOLOv8n model. At the same time, precision, recall, and F1 score have also significantly improved. While maintaining the real-time performance of the model, it effectively enhances the detection ability of micro-defects and overall robustness. The SBC-YOLOv8n model proposed in this study significantly improves the comprehensive performance of photovoltaic cell defect detection through the collaborative optimization of attention mechanisms, feature fusion structures, and loss functions, meeting the real-time detection requirements of industry and providing reliable technical support for photovoltaic manufacturing quality control.