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

Abstract

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 attention-relation 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.