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Frontiers of Information Technology & Electronic Engineering >> 2023, Volume 24, Issue 2 doi: 10.1631/FITEE.2200524

A graph-based two-stage classification network for mobile screen defect inspection

State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China; College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China; Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, Xi’an 710049, China; less

Received:2022-10-31 Accepted: 2023-02-27 Available online:2023-02-27

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Defect inspection, also known as defect detection, is significant in mobile screen quality control. There are some challenging issues brought by the characteristics of screen defects, including the following: (1) the problem of interclass similarity and intraclass variation, (2) the difficulty in distinguishing low contrast, tiny-sized, or incomplete defects, and (3) the modeling of category dependencies for multi-label images. To solve these problems, a graph reasoning module, stacked on a classification module, is proposed to expand the feature dimension and improve low-quality image features by exploiting category-wise dependency, image-wise relations, and interactions between them. To further improve the classification performance, the classifier of the classification module is redesigned as a cosine similarity function. With the help of contrastive learning, the classification module can better initialize the category-wise graph of the reasoning module. Experiments on the mobile screen defect dataset show that our two-stage network achieves the following best performances: 97.7% accuracy and 97.3% -measure. This proves that the proposed approach is effective in industrial applications.

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