Attention-relation network for mobile phone screen defect classification via a few samples  

作　　者：Jiao Mao Guoliang Xu Lijun He Jiangtao Luo 

机构地区：[1]College of Communication and Information Engineering,Chongqing University of Posts and Telecommunications,Chongqing,400065,China [2]Institute of Electronic Information and Networking Engineering,Chongqing University of Posts and Telecommunications,Chongqing,400065,China

出　　处：《Digital Communications and Networks》2024年第4期1113-1120,共8页数字通信与网络（英文版）

摘　　要：How to use a few defect samples to complete the defect classification is a key challenge in the production of mobile phone screens.An attention-relation network for the mobile phone screen defect classification is proposed in this paper.The architecture of the attention-relation network contains two modules:a feature extract module and a feature metric module.Different from other few-shot models,an attention mechanism is applied to metric learning in our model to measure the distance between features,so as to pay attention to the correlation between features and suppress unwanted information.Besides,we combine dilated convolution and skip connection to extract more feature information for follow-up processing.We validate attention-relation network on the mobile phone screen defect dataset.The experimental results show that the classification accuracy of the attentionrelation network is 0.9486 under the 5-way 1-shot training strategy and 0.9039 under the 5-way 5-shot setting.It achieves the excellent effect of classification for mobile phone screen defects and outperforms with dominant advantages.

关 键 词：Mobile phone screen defects A few samples Relation network Attention mechanism Dilated convolution 

分 类 号：TN929.53[电子电信—通信与信息系统] TP391.41[电子电信—信息与通信工程]