摘要
电致发光(Electroluminescence,EL)下的光伏电池EL图像背景表现为复杂的非均匀纹理特征,且存在与裂纹相似的晶粒伪缺陷,同时裂纹表现为形状多样的多尺度特征,以上难点为检测任务带来了极大的挑战。因此,本文提出融合注意力的多尺度Faster-RCNN模型,一方面,采用改进的特征金字塔网络获取多尺度的高级语义特征图,以此来提高网络对多尺度裂纹缺陷的特征表达能力。另一方面,采用改进的注意力区域推荐网络A-RPN,提高模型对裂纹缺陷的关注并抑制复杂背景及晶粒伪缺陷的特征。同时,在RPN网络训练过程中,采用损失函数Focal loss,以此来降低训练过程中简单样本所占比重,使其更加关注难以区分的样本。实验结果表明,改进的算法使得EL图像裂纹缺陷检测的准确率提高,达到接近95%。
The background of the EL image of a photovoltaic cell under electroluminescence(EL)presents complex non-uniform texture features,and there are grain pseudo-defects similar to cracks.At the same time,the cracks appear as multi-scale features with various shapes.The above mentioned difficulties have presented great challenges for the detection task.Therefore,this paper proposes a multi-scale Faster-RCNN model that integrates attention.On the one hand,an improved feature pyramid network is used to obtain multi-scale advanced semantic feature maps to improve the network's feature expression ability of multi-scale crack defects.On the other hand,an improved attention region proposal network A-RPN is adopted to increase the model's attention to crack defects and suppress the characteristics of complex background and grain pseudo-defects.At the same time,in the RPN network training process,a loss function Focal loss is used to reduce the proportion of simple samples in the training process,so that the model pays more attention to the samples that are difficult to distinguish.Experimental results show that this algorithm improves the accuracy of crack defect detection in EL images,reaching nearly 95%.
作者
陈海永
赵鹏
闫皓炜
Chen Haiyong;Zhao Peng;Yan Haowei(School of Artificial Intelligence,Hebei University of Technology,Tianjin 300000,China;Tianjin Aerospace Zhongwei Data System Technology Co.,Ltd,Tianjin 300000,China)
出处
《光电工程》
CAS
CSCD
北大核心
2021年第1期61-71,共11页
Opto-Electronic Engineering
基金
国家自然科学基金资助项目(61873315)。
