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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: 2078
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.
[1]Bottou L, 2010. Large-scale machine learning with stochastic gradient descent. Proc 19th Int Conf on Computational Statistics, p.177-186.
[2]Chen T, Kornblith S, Norouzi M, et al., 2020. A simple framework for contrastive learning of visual representations. Proc 37th Int Conf on Machine Learning, p.1597-1607.
[3]Chen ZM, Wei XS, Wang P, et al., 2019. Multi-label image recognition with graph convolutional networks. Proc IEEE/CVF Conf on Computer Vision and Pattern Recognition, p.5177-5186.
[4]Gidaris S, Komodakis N, 2018. Dynamic few-shot visual learning without forgetting. Proc IEEE/CVF Conf on Computer Vision and Pattern Recognition, p.4367-4375.
[5]Haurum JB, Moeslund TB, 2021. Sewer-ML: a multi-label sewer defect classification dataset and benchmark. Proc IEEE/CVF Conf on Computer Vision and Pattern Recognition, p.13451-13462.
[6]He KM, Zhang XY, Ren SQ, et al., 2016. Deep residual learning for image recognition. Proc IEEE Conf on Computer Vision and Pattern Recognition, p.770-778.
[7]He KM, Fan HQ, Wu YX, et al., 2020. Momentum contrast for unsupervised visual representation learning. Proc IEEE/CVF Conf on Computer Vision and Pattern Recognition, p.9726-9735.
[8]Hjelm RD, Fedorov A, Lavoie-Marchildon S, et al., 2019. Learning deep representations by mutual information estimation and maximization. Proc 7th Int Conf on Learning Representations.
[9]Khosla P, Teterwak P, Wang C, et al., 2020. Supervised contrastive learning. Proc 34th Conf on Neural Information Processing Systems, p.18661-18673.
[10]Kong YH, Liu X, Zhao ZB, et al., 2022. Bolt defect classification algorithm based on knowledge graph and feature fusion. Energy Rep, 8(Suppl 1):856-863.
[11]Lei J, Gao X, Feng ZL, et al., 2018. Scale insensitive and focus driven mobile screen defect detection in industry. Neurocomputing, 294:72-81.
[12]Li CS, Zhang XM, Huang YJ, et al., 2020. A novel algorithm for defect extraction and classification of mobile phone screen based on machine vision. Comput Ind Eng, 146:106530.
[13]Lu Y, Ma L, Jiang HQ, 2020. A light CNN model for defect detection of LCD. In: Hung JC, Yen NY, Chang JW (Eds.), Frontier Computing. Springer, Singapore, p.10-19.
[14]Park JY, Hwang Y, Lee D, et al., 2020. MarsNet: multi-label classification network for images of various sizes. IEEE Access, 8:21832-21846.
[15]Paszke A, Gross S, Chintala S, et al., 2017. Automatic differentiation in PyTorch. Proc 31st Conf on Neural Information Processing Systems.
[16]Simonyan K, Zisserman A, 2015. Very deep convolutional networks for large-scale image recognition. Proc 3rd Int Conf on Learning Representations.
[17]Szegedy C, Vanhoucke V, Ioffe S, et al., 2016. Rethinking the inception architecture for computer vision. Proc IEEE Conf on Computer Vision and Pattern Recognition, p.2818-2826.
[18]Wang T, Zhang C, Ding RW, et al., 2021. Mobile phone surface defect detection based on improved faster R-CNN. Proc 25th Int Conf on Pattern Recognition, p.9371-9377.
[19]Wang Y, He DL, Li F, et al., 2020. Multi-label classification with label graph superimposing. Proc AAAI Conf Artif Intell, 34(7):12265-12272. doi:
[20]Wang YC, Gao L, Li XY, et al., 2021a. A new graph-based method for class imbalance in surface defect recognition. IEEE Trans Instrum Meas, 70:5007816.
[21]Wang YC, Gao L, Gao YP, et al., 2021b. A new graph-based semi-supervised method for surface defect classification. Rob Comput Integr Manuf, 68:102083.
[22]Wei B, Hao KR, Gao L, et al., 2021. Bioinspired visual-integrated model for multilabel classification of textile defect images. IEEE Trans Cognit Dev Syst, 13(3):503-513.
[23]Wu ZR, Xiong YJ, Yu SX, et al., 2018. Unsupervised feature learning via non-parametric instance discrimination. Proc IEEE/CVF Conf on Computer Vision and Pattern Recognition, p.3733-3742.
[24]Xiao WW, Song KC, Liu J, et al., 2022. Graph embedding and optimal transport for few-shot classification of metal surface defect. IEEE Trans Instrum Meas, 71:5010310.
[25]Xu H, Jiang CH, Liang XD, et al., 2019. Reasoning-RCNN: unifying adaptive global reasoning into large-scale object detection. Proc IEEE/CVF Conf on Computer Vision and Pattern Recognition, p.6412-6421.
[26]Yuan ZC, Zhang ZT, Su H, et al., 2018. Vision-based defect detection for mobile phone cover glass using deep neural networks. Int J Precis Eng Manuf, 19(6):801-810.
[27]Zhang JB, Su H, Zou W, et al., 2021. CADN: a weakly supervised learning-based category-aware object detection network for surface defect detection. Patt Recogn, 109:107571.
[28]Zhao JW, Yan K, Zhao YF, et al., 2021. Transformer-based dual relation graph for multi-label image recognition. Proc IEEE/CVF Int Conf on Computer Vision, p.163-172.
[29]Zhou BL, Khosla A, Lapedriza A, et al., 2016. Learning deep features for discriminative localization. Proc IEEE Conf on Computer Vision and Pattern Recognition, p.2921-2929.
[30]Zhu Y, Ding RW, Huang WB, et al., 2022. HMFCA-Net: hierarchical multi-frequency based channel attention net for mobile phone surface defect detection. Patt Recogn Lett, 153:118-125.
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