现代电子技术2026，Vol.49Issue(13)：154-163,10.DOI:10.16652/j.issn.1004-373X.2026.13.023
基于RepED-YOLOv8n的太阳能电池缺陷检测算法
Solar cell defect detection based on RepED-YOLOv8n
摘要
Abstract
The existence of small object defects on the surface of solar cells and the variety of defect morphology will cause efficiency loss of the power generation system.In view of this,the thesis proposes a novel solar cell defect detection algorithm RepED-YOLOv8n based on YOLOv8n.Firstly,by deeply integrating the ESE mechanism into the SE layer of MBConv,an EMBC is innovatively constructed to replace the original C2f module,which realizes the improvement of the detection accuracy while maintaining a small number of parameters.Secondly,the YOLOv8n neck network is reconstructed by Efficient RepGFPN;by integrating CSPNet,the efficient layer aggregation network(ELAN),and re-parameterization techniques,and introducing a deformable large kernel attention(DLKA)module,the model can well adapt to capture the complex and subtle defect features on solar cell surfaces,which results in a novel architecture termed the reparameterized generalized feature pyramid network with DLKA(RepGFPN-DLKA).Finally,the WIoU v3 loss function is used to pay more attention to small object defects and eliminate uneven label distribution in the dataset.The experiments show that the mAP@0.5 of the proposed model in the experimental dataset reached 89.2%,an improvement of 2.4%over that of the baseline model.Its precision and recall rate reach 82.5%and 87.3%,respectively,which are improved by 3.1%and 3.7%.The detection performance of the proposed model is improved significantly while keeping a small number of parameters.关键词
太阳能电池缺陷检测/RepED-YOLOv8n/RepGFPN-DLKA/重参数/可变形大核注意力/损失函数Key words
solar cell defect detection/RepED-YOLOv8n/RepGFPN-DLKA/reparameterization/DLKA/loss function分类
信息技术与安全科学引用本文复制引用
王佩,平佳熠,李渊华,林佳..基于RepED-YOLOv8n的太阳能电池缺陷检测算法[J].现代电子技术,2026,49(13):154-163,10.基金项目
国家自然科学基金项目(62475145) （62475145）
上海市教育发展基金会和上海市教育委员会曙光计划(24SG53) （24SG53）
