Optics and Precision Engineering, Volume. 31, Issue 22, 3357(2023)
A lightweight deep learning model for TFT-LCD circuits defect classification based on swin transformer
[1] HARALICK R M, SHANMUGAM K, DINSTEIN I. Textural features for image classification[J]. IEEE Transactions on Systems, Man, and Cybernetics, 610-621(1973).
[2] DALAL N, TRIGGS B. Histograms of oriented gradients for human detection[C], 886-893(20).
[3] STOCKMAN G, SHAPIRO LG[M]. Computer Vision, 69-73(2002).
[4] PLATT J C[standard](1998).
[5] KANG S B, LEE J H, SONG K Y et al. Automatic defect classification of TFT-LCD panels using machine learning[C], 2175-2177(5).
[6] HUANG W, LU H T. Defect Classification of TFT-LCD with bag of visual words approach[C], 167-170(27).
[7] KONG L F, SHEN J, HU Z L et al. Detection of Water-Stains defects in TFT-LCD based on machine vision[C], 1-5(13).
[8] [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] [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] CHIKONTWE P, KIM S, PARK S H. CAD: Co-Adapting discriminative features for improved Few-Shot classification[C], 14534-14543(18).
[12] CHEN W, GAO Y, GAO L et al. A new ensemble approach based on deep convolutional neural networks for steel surface defect classification[J]. Procedia CIRP, 72, 1069-1072(2018).
[13] FU G, SUN P, ZHU W et al. A deep-learning-based approach for fast and robust steel surface defects classification[J]. Optics and Lasers in Engineering, 121, 397-405(2019).
[14] KONOVALENKO I, MARUSCHAK P, BREZINOVÁ J et al. Steel surface defect classification using deep residual neural network[J]. Metals, 10, 846(2020).
[15] HE D, XU K, WANG D. Design of multi-scale receptive field convolutional neural network for surface inspection of hot rolled steels[J]. Image and Vision Computing, 89, 12-20(2019).
[16] HASELMANN M, GRUBER D. Supervised machine learning based surface inspection by synthetizing artificial defects[C], 390-395(18).
[17] LIU Z, LIN Y T, CAO Y et al. Swin transformer: hierarchical vision transformer using shifted windows[C], 9992-10002(10).
[20] CHOLLET F. Xception: deep learning with depthwise separable convolutions[C], 1800-1807(21).
[22] HE K M, ZHANG X Y, REN S Q et al. Deep residual learning for image recognition[C], 770-778(27).
[23] HUANG G, LIU Z, VAN DER MAATEN L et al. Densely connected convolutional networks[C], 2261-2269(21).
[26] LIU Z, MAO H Z, WU C Y et al. A ConvNet for the 2020s[C], 11966-11976(18).
[27] DEBNATH S, HU R H et al. ConvNeXt V2: Co-Designing and scaling convnets with masked autoencoders[C], 16133-16142(17).
[28] FANG Y X, WANG W, XIE B H et al. EVA: exploring the limits of masked visual representation learning at scale[C], 19358-19369(17).
Get Citation
Copy Citation Text
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
Category:
Received: Mar. 30, 2023
Accepted: --
Published Online: Dec. 29, 2023
The Author Email: Chen LUO (chenluo@seu.edu.cn)