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CLC number: TP391.4
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2022-11-28
Cited: 0
Clicked: 2080
Citations: Bibtex RefMan EndNote GB/T7714
A graph-based two-stage classification network for mobile screen defect inspection
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Chaofan ZHOU, Meiqin LIU, Senlin ZHANG, Ping WEI, Badong CHEN. A graph-based two-stage classification network for mobile screen defect inspection[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(2): 203-216.
@article{title="A graph-based two-stage classification network for mobile screen defect inspection",
author="Chaofan ZHOU, Meiqin LIU, Senlin ZHANG, Ping WEI, Badong CHEN",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="24",
number="2",
pages="203-216",
year="2023",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2200524"
}
%0 Journal Article
%T A graph-based two-stage classification network for mobile screen defect inspection
%A Chaofan ZHOU
%A Meiqin LIU
%A Senlin ZHANG
%A Ping WEI
%A Badong CHEN
%J Frontiers of Information Technology & Electronic Engineering
%V 24
%N 2
%P 203-216
%@ 2095-9184
%D 2023
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2200524
TY - JOUR
T1 - A graph-based two-stage classification network for mobile screen defect inspection
A1 - Chaofan ZHOU
A1 - Meiqin LIU
A1 - Senlin ZHANG
A1 - Ping WEI
A1 - Badong CHEN
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 24
IS - 2
SP - 203
EP - 216
%@ 2095-9184
Y1 - 2023
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2200524
Abstract: 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% F-measure. This proves that the proposed approach is effective in industrial applications.
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