Optics and Precision Engineering, Volume. 31, Issue 10, 1563(2023)

Textile defect recognition network based on label embedding

Ying LIU*, Wei JIANG, Guandian LI, Lei CHEN, and Shuang ZHAO
Author Affiliations
  • College of Electronic Information Engineering, Changchun University of Science and Technology, Changchun130000, China
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    References(36)

    [1] [1] 1庄集超. 基于深度卷积神经网络的布匹瑕疵点检测算法研究[D]. 秦皇岛: 燕山大学, 2020. doi: 10.1109/ccdc52312.2021.9601431ZHUANGJ C. Research on Detection Algorithm Based on Deep Convolution Neural Network for Fabric Defects[D]. Qinhuangdao: Yanshan University, 2020. (in Chinese). doi: 10.1109/ccdc52312.2021.9601431

    [2] CHEN J H, JAIN A K. A structural approach to identify defects in textured images[C], 29-32(8).

    [3] COHEN F S, FAN Z, ATTALI S. Automated inspection of textile fabrics using textural models[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13, 803-808(1991).

    [4] [4] 4黄佩璠. 基于深度学习的布匹瑕疵检测识别的研究和应用[D]. 南昌: 南昌大学, 2021.HUANGP F. Research and Application of Fabric Defect Detection and Recognition Based on Deep Learning[D]. Nanchang: Nanchang University, 2021. (in Chinese)

    [5] CHIU S H, CHOU S, LIAW J J et al. Textural defect segmentation using a Fourier-domain maximum likelihood estimation method[J]. Textile Research Journal, 72, 253-258(2002).

    [6] JING J F, FAN X T, LI P F. Automated fabric defect detection based on multiple Gabor filters and KPCA[J]. International Journal of Multimedia and Ubiquitous Engineering, 11, 93-106(2016).

    [7] LIANG Z, XU B, CHI Z et al. Intelligent characterization and evaluation of yarn surface appearance using saliency map analysis, wavelet transform and fuzzy ARTMAP neural network[J]. Expert Systems With Applications, 39, 4201-4212(2012).

    [8] [8] 8陈彦彤, 陈伟楠, 张献中, 等. 基于深度卷积神经网络的蝇类面部识别[J]. 光学 精密工程, 2020, 28(7): 1558-1567. doi: 10.37188/OPE.20202807.1558CHENY T, CHENW N, ZHANGX Z, et al. Fly facial recognition based on deep convolutional neural network[J]. Opt. Precision Eng., 2020, 28(7): 1558-1567. (in Chinese). doi: 10.37188/OPE.20202807.1558

    [9] [9] 9王宸, 张秀峰, 刘超, 等. 改进YOLOv3的轮毂焊缝缺陷检测[J]. 光学 精密工程, 2021, 29(8): 1942-1954. doi: 10.37188/OPE.20212908.1942WANGC, ZHANGX F, LIUC, et al. Detection method of wheel hub weld defects based on the improved YOLOv3[J]. Opt. Precision Eng., 2021, 29(8): 1942-1954. (in Chinese). doi: 10.37188/OPE.20212908.1942

    [10] JI L Y, JIANG X Y, GAO Y B et al. ADR-Net: context extraction network based on M-Net for medical image segmentation[J]. Medical Physics, 47, 4254-4264(2020).

    [11] WANG C Y, LI L F. Multi-scale residual deep network for semantic segmentation of buildings with regularizer of shape representation[J]. Remote Sensing, 12, 2932(2020).

    [12] LIU W J, ZHANG Y J, FAN H S et al. A new multi-channel deep convolutional neural network for semantic segmentation of remote sensing image[J]. IEEE Access, 8, 131814-131825(2020).

    [13] [13] 13陈欣, 万敏杰, 马超, 等. 采用多尺度特征融合SSD的遥感图像小目标检测[J]. 光学 精密工程, 2021, 29(11): 2672-2682. doi: 10.37188/OPE.20212911.2672CHENX, WANM J, MAC, et al. Recognition of small targets in remote sensing image using multi-scale feature fusion-based shot multi-box detector[J]. Opt. Precision Eng., 2021, 29(11): 2672-2682. (in Chinese). doi: 10.37188/OPE.20212911.2672

    [14] [14] 14王阳, 陈薇伊, 马军山. 基于卷积神经网络的乳腺癌良恶性诊断[J]. 软件工程, 2022, 25(1): 6-9.WANGY, CHENW Y, MAJ S. Diagnosis of benign and malignant breast cancer based on convolutional neural network[J]. Software Engineer, 2022, 25(1): 6-9. (in Chinese)

    [15] UZEN H, TURKOGLU M, HANBAY D. Texture defect classification with multiple pooling and filter ensemble based on deep neural network[J]. Expert Systems With Applications, 175, 114838(2021).

    [16] [16] 16余永维, 韩鑫, 杜柳青. 基于Inception-SSD算法的零件识别[J]. 光学 精密工程, 2020, 28(8): 1799-1809.YUY W, HANX, DUL Q. Target part recognition based Inception-SSD algorithm[J]. Opt. Precision Eng., 2020, 28(8): 1799-1809. (in Chinese)

    [17] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 60, 84-90(2017).

    [18] HE K M, ZHANG X Y, REN S Q et al. Deep Residual Learning for Image Recognition[C], 770-778(27).

    [19] HU J, SHEN L, SUN G. Squeeze-and-Excitation Networks[C], 7132-7141(18).

    [20] OUYANG W B, XU B G, HOU J et al. Fabric defect detection using activation layer embedded convolutional neural network[J]. IEEE Access, 7, 70130-70140(2019).

    [21] [21] 21陆贵家. 基于Cascade R-CNN改进的花色布匹瑕疵智能识别方法[J]. 现代信息科技, 2020, 4(23): 20-24.LUG J. Improved intelligent recognition method of pattern and color fabric defects based on cascade R-CNN[J]. Modern Informationn Technology, 2020, 4(23): 20-24. (in Chinese)

    [22] PENG P R, WANG Y, HAO C et al. Automatic fabric defect detection method using PRAN-net[J]. Applied Sciences, 10, 8434(2020).

    [24] SMITH L N. Cyclical Learning Rates for Training Neural Networks[C], 464-472(2017).

    [25] TAN M, LE Q. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks[C], 6105-6114(2019).

    [26] HUANG G, LIU Z, VAN DER MAATEN L et al. Densely Connected Convolutional Networks[C], 2261-2269(21).

    [27] XIE S N, GIRSHICK R, DOLLÁR P et al. Aggregated Residual Transformations for Deep Neural Networks[C], 5987-5995(21).

    [28] ZAGORUYKO S, KOMODAKIS N. Wide residual networks[J]. arXiv preprint(2016).

    [29] DOSOVITSKIY A, BEYER L, KOLESNIKOV A et al. An image is worth 16x16 words: Transformers for image recognition at scale[J]. arXiv preprint(2020).

    [30] SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[J]. arXiv preprint(2014).

    [31] ZHENG H L, FU J L, MEI T et al. Learning Multi-Attention Convolutional Neural Network for Fine-Grained Image Recognition[C], 5219-5227(22).

    [32] FU J L, ZHENG H L, MEI T. Look Closer to See Better: Recurrent Attention Convolutional Neural Network for Fine-Grained Image Recognition[C], 4476-4484(21).

    [33] HU T, QI H, HUANG Q et al. See better before looking closer: Weakly supervised data augmentation network for fine-grained visual classification[J]. arXiv preprint(2019).

    [34] ZHENG H L, FU J L, ZHA Z J et al. Looking for the Devil in the Details: Learning Trilinear Attention Sampling Network for Fine-Grained Image Recognition[C], 5007-5016(15).

    [35] CHEN Y, BAI Y L, ZHANG W et al. Destruction and Construction Learning for Fine-Grained Image Recognition[C], 5152-5161(15).

    [36] HE J, CHEN J N, LIU S et al. TransFG: a transformer architecture for fine-grained recognition[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 36, 852-860(2022).

    CLP Journals

    [1] Xiaodong SUN, Qibing ZHU, Huawei XU, Tongzhen XING, Haibin ZHU. MFL_YOLOv8 algorithm for surface defect detection of microfiber leather[J]. Optics and Precision Engineering, 2025, 33(2): 311

    [2] Xiaodong SUN, Qibing ZHU, Huawei XU, Tongzhen XING, Haibin ZHU. MFL_YOLOv8 algorithm for surface defect detection of microfiber leather[J]. Optics and Precision Engineering, 2025, 33(2): 311

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    Ying LIU, Wei JIANG, Guandian LI, Lei CHEN, Shuang ZHAO. Textile defect recognition network based on label embedding[J]. Optics and Precision Engineering, 2023, 31(10): 1563

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    Paper Information

    Category: Information Sciences

    Received: Jun. 10, 2022

    Accepted: --

    Published Online: Jul. 4, 2023

    The Author Email: Ying LIU (liuying02@cust.edu.cn)

    DOI:10.37188/OPE.20233110.1563

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