• Optics and Precision Engineering
  • Vol. 31, Issue 22, 3357 (2023)
Yan XIA1, Chen LUO1,*, Yijun ZHOU1, and Lei JIA2
Author Affiliations
  • 1School of Mechanical Engineering, Southeast University, Nanjing289, China
  • 2Wuxi Shangshi-finevision Technology Co., Ltd, Wuxi14174, China
  • show less
    DOI: 10.37188/OPE.20233122.3357 Cite this Article
    Yan XIA, Chen LUO, Yijun ZHOU, Lei JIA. A lightweight deep learning model for TFT-LCD circuits defect classification based on swin transformer[J]. Optics and Precision Engineering, 2023, 31(22): 3357 Copy Citation Text show less
    References

    [1] R M HARALICK, K SHANMUGAM, I DINSTEIN. Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, 610-621(1973).

    [2] N DALAL, B TRIGGS. Histograms of oriented gradients for human detection, 886-893(20).

    [3] G STOCKMAN, LG SHAPIRO. Computer Vision, 69-73(2002).

    [4] J C PLATT(1998).

    [5] S B KANG, J H LEE, K Y SONG et al. Automatic defect classification of TFT-LCD panels using machine learning, 2175-2177(5).

    [6] W HUANG, H T LU. Defect Classification of TFT-LCD with bag of visual words approach, 167-170(27).

    [7] L F KONG, J SHEN, Z L HU et al. Detection of Water-Stains defects in TFT-LCD based on machine vision, 1-5(13).

    [8] 肖术明, 王绍举, 常琳, 等. 面向手写数字图像的压缩感知快速分类[J]. 光学 精密工程, 2021, 29(7): 1709-1719. doi: 10.37188/OPE.20212907.1709XIAOS M, WANGS J, CHANGL, et al. Compressive sensing fast classification for handwritten digital images[J]. Opt. Precision Eng., 2021, 29(7): 1709-1719.(in Chinese). doi: 10.37188/OPE.20212907.1709

    [9] 苗传开, 娄树理, 李婷, 等. 基于弱监督学习的多标签红外图像分类算法[J]. 光学 精密工程, 2022, 30(20): 2501-2509. doi: 10.37188/ope.20223020.2501MIAOC K, LOUS L, LIT, et al. Multi-label infrared image classification algorithm based on weakly supervised learning[J]. Opt. Precision Eng., 2022, 30(20): 2501-2509. (in Chinese). doi: 10.37188/ope.20223020.2501

    [10] P CHIKONTWE, S KIM, S H PARK. CAD: Co-Adapting discriminative features for improved Few-Shot classification, 14534-14543(18).

    [11] Z ZHANG, Z XUE, Y CHEN et al. Boosting Verified Training for Robust Image Classifications via Abstraction. arXiv, 2303-11552(2023). https://arxiv.org/abs/2303.11552.pdf

    [12] W CHEN, Y GAO, L GAO et al. A new ensemble approach based on deep convolutional neural networks for steel surface defect classification. Procedia CIRP, 72, 1069-1072(2018).

    [13] G FU, P SUN, W ZHU et al. A deep-learning-based approach for fast and robust steel surface defects classification. Optics and Lasers in Engineering, 121, 397-405(2019).

    [14] I KONOVALENKO, P MARUSCHAK, J BREZINOVÁ et al. Steel surface defect classification using deep residual neural network. Metals, 10, 846(2020).

    [15] D HE, K XU, D WANG. Design of multi-scale receptive field convolutional neural network for surface inspection of hot rolled steels. Image and Vision Computing, 89, 12-20(2019).

    [16] M HASELMANN, D GRUBER. Supervised machine learning based surface inspection by synthetizing artificial defects, 390-395(18).

    [17] Z LIU, Y T LIN, Y CAO et al. Swin transformer: hierarchical vision transformer using shifted windows, 9992-10002(10).

    [18] A DOSOVITSKIY, L BEYER, A KOLESNIKOV et al. An image is worth. arXiv, 2010-11929(2020). https://arxiv.org/abs/2010.11929.pdf

    [19] D BOLYA, C FU, X DAI et al. Token Merging your ViT but Faster. arXiv, 2210-09461(2022). https://arxiv.org/abs/2210.09461.pdf

    [20] F CHOLLET. Xception: deep learning with depthwise separable convolutions, 1800-1807(21).

    [21] G HINTON, O VINYALS, J DEAN. Distilling The Knowledge in a Neural Network. arXiv, 1503-02531(2015). https://arxiv.org/abs/1503.02531.pdf

    [22] K M HE, X Y ZHANG, S Q REN et al. Deep residual learning for image recognition, 770-778(27).

    [23] G HUANG, Z LIU, L VAN DER MAATEN et al. Densely connected convolutional networks, 2261-2269(21).

    [24] M TAN, Q V LE. EfficientNetV2: Smaller Models and Faster Training. arXiv, 2104-00298(2021). https://arxiv.org/abs/2104.00298.pdf

    [25] S MEHTA, M RASTEGARI. Separable Self-Attention for Mobile Vision Transformers. arXiv, 2206-02680(2022). https://arxiv.org/abs/2206.02680.pdf

    [26] Z LIU, H Z MAO, C Y WU et al. A ConvNet for the 2020s, 11966-11976(18).

    [27] S DEBNATH, R H HU et al. ConvNeXt V2: Co-Designing and scaling convnets with masked autoencoders, 16133-16142(17).

    [28] Y X FANG, W WANG, B H XIE et al. EVA: exploring the limits of masked visual representation learning at scale, 19358-19369(17).

    [29] Y FANG, Q SUN, X WANG et al. EVA-02: a Visual Representation for Neon Genesis. arXiv, 2303-11331(2023). https://arxiv.org/abs/2303.11331.pdf

    Yan XIA, Chen LUO, Yijun ZHOU, Lei JIA. A lightweight deep learning model for TFT-LCD circuits defect classification based on swin transformer[J]. Optics and Precision Engineering, 2023, 31(22): 3357
    Download Citation