华南理工大学学报（自然科学版）2026，Vol.54Issue(5)：37-46,10.DOI:10.12141/j.issn.1000-565X.250294
基于SBC-YOLOv8n的光伏电池片缺陷检测
Defect Detection of Photovoltaic Cells Based on SBC-YOLOv8n
摘要
Abstract
The surface defects of photovoltaic cells are characterized by fine scales and significant overlap with background features,leading to frequent missed detections and false alarms in traditional detection algorithms.Moreover,the robustness of defect recognition in existing methods urgently requires improvement.This paper pro-poses an improved model based on YOLOv8n,termed SBC-YOLOv8n(SE-BiFPN-CA-YOLOv8n),to enhance recog-nition accuracy and stability in complex industrial scenarios,thereby strengthening the detection capability for mi-nute defects on photovoltaic cell surfaces.First,a dual-attention mechanism combining coordinate attention and squeeze-and-excitation attention is integrated before and after the SPPF module in the backbone network.This en-ables the feature extraction ability for minute defects from two dimensions:spatial location perception and channel-wise weight adjustment.Second,the original neck network structure is replaced with a weighted bidirectional fea-ture pyramid network(BiFPN),which simplifies nodes and adopts a bidirectional weighted fusion mechanism to rein-force multi-scale feature interaction while suppressing background interference.Finally,to address class imba-lance,the focal loss function is optimized by increasing the focusing parameter γ from 2 to 3 to enhance the atten-tion on hard-to-classify samples,and adjusting the class balance factor α from 0.25 to 0.5 to substantially raise the loss weight of defect samples while reducing that of the background.The improved SBC-YOLOv8n model achieves an mAP0.5 of 80.2%on the test set,representing a 4.2 percentage point improvement over the original YOLOv8n model.Meanwhile,precision,recall,and F1 score are significantly enhanced,effectively improving the detection capability of minute defects and overall robustness while maintaining the model's real-time performance.关键词
缺陷检测/YOLOv8n/深度学习/坐标注意力机制/压缩与激励注意力机制/焦点损失函数Key words
defect detection/YOLOv8n/deep learning/coordinate attention mechanism/squeeze-and-excitation attention mechanism/focal loss function分类
信息技术与安全科学引用本文复制引用
郭建,谢鹤鸣,钟琪峰,温雅晴..基于SBC-YOLOv8n的光伏电池片缺陷检测[J].华南理工大学学报（自然科学版）,2026,54(5):37-46,10.基金项目
广东省科技创新战略专项资金项目(pdjh2025bk306)Supported by the Guangdong Province Special Fund for Science and Technology Innovation Strategy(pdjh2025bk306) （pdjh2025bk306）
