A Photovoltaic Cell Defect Detection Method Using Electroluminescent and Googlenet

摘要：

　　Electroluminescent(EL)plays an important role in the application of photovoltaic cell Defect detection.Traditional approaches for EL result analysis usually utilize visual inspection by technicians and have the drawbacks of low efficiency which can be improved by employing deep convolutional neural network(CNN)features that contain more semantic and structure information and thus possess more discriminative ability.Therefore,a defect detection method based on EL and GoogLeNet is proposed in this work.Firstly,a database of EL image samples for photovoltaic cell defects is built,then a deep convolutional neural network based on GoogLeNet is established.At last,the experiments and simulation tests prove that the presented defect detection approach is superior to the conventional methods.The detection precision is more than 85%,while the previous accuracy is under 67%.Whats more,the proposed method is more stable and efficient.

关键词： Photovoltaic cell defect detection Convolutional Neural Network CNN Electroluminescent (EL) GoogLeNet

作者: Binhui Liu Qiangrong Yang Yurong Han

作者单位: Quality Inspection and Testing Center,The Fifth Institute of MIIT Pony.ai

会议类型: 国际会议

会议名称: 2019 2nd International Conference on Mechanical Engineering, Industrial Materials and Industrial Electronics (MEIMIE 2019)2019年第二届机械工程、工业材料和工业电子国际会议(Meimie 2019)

会议地点: 大连

会议语种:英文

页码: 158-166

在线出版日期: 2019-03-29（万方平台首次上网日期，不代表论文的发表时间）