• Laser & Optoelectronics Progress
  • Vol. 57, Issue 10, 101014 (2020)
Jian Xu, Shupei Wu*, and Xiuping Liu
Author Affiliations
  • School of Electronics Information, Xi'an Polytechnic University, Xi'an, Shaanxi 710048, China
  • show less
    DOI: 10.3788/LOP57.101014 Cite this Article Set citation alerts
    Jian Xu, Shupei Wu, Xiuping Liu. Classification of Bobbins Based on Improved Deep Neural Network[J]. Laser & Optoelectronics Progress, 2020, 57(10): 101014 Copy Citation Text show less
    References

    [1] Ji J Z, Liu J L, Gao W D et al. Measurement of yarn linear density based on digital image processing[J]. Journal of Textile Research, 32, 42-46(2011).

    [2] Ozkaya Y A. Digital image processing and illumination techniques for yarn characterization[J]. Journal of Electronic Imaging, 14, 023001(2005).

    [3] Zhang F, Zhang T S, Ji Y L et al. Research on color sorting algorithm of spinning tube based on machine vision[J]. Journal of Xi'an Polytechnic University, 32, 560-566(2018).

    [4] Yang L Z, Zhou F Y. Clustering method for bobbin based on machine vision[J]. Wool Textile Journal, 45, 85-88(2017).

    [5] Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks. [C]∥Advances in neural information processing systems, December 3-6, 2012, Lake Tahoe, Nevada, United States. New York: Curran Associates, 1097-1105(2012).

    [6] Simonyan K. -04-10)[2019-08-09]. https:∥arxiv.xilesou., top/abs/1409, 1556(2015).

    [7] LeCun Y, Bottou L, Bengio Y et al. Gradient-based learning applied todocument recognition[J]. Proceedings of the IEEE, 86, 2278-2324(1998).

    [8] Szegedy C, Liu W, Jia Y et al. Going deeper with convolutions. [C]∥Proceedings of the IEEE conference on computer vision and pattern recognition, June 20-25, 2015, Miami, FL, USA. New York: IEEE, 1-9(2015).

    [9] He K, Zhang X, Ren S et al. Deep residual learning for image recognition. [C]∥Proceedings of the IEEE conference on computer vision and pattern recognition, December 10-14, 2015, San Diego, USA. New York: IEEE, 770-778(2016).

    [10] Zhuo D, Jing J F, Zhang H H et al. Classification of chopped strand mat defects based on convolutional neural network[J]. Laser & Optoelectronics Progress, 56, 101009(2019).

    [11] Chu J H, Wu Z R, Lü W et al. Breast cancer diagnosis system based on transfer learning and deep convolutional neural networks[J]. Laser & Optoelectronics Progress, 55, 081001(2018).

    [12] Ma Y J, Li X Y, Song X F. Traffic sign recognition based on improved deep convolution neural network[J]. Laser & Optoelectronics Progress, 55, 121009(2018).

    [13] Chen Q J, Li Y, Chai Y Z. A multi-focus image fusion algorithm based on depth learning[J]. Laser & Optoelectronics Progress, 55, 071015(2018).

    [14] Zhou T S, Dang P F, Xie H. Research on remote sensing image classification based on improved AlexNet network model[J]. Beijing Surveying and Mapping, 32, 1263-1266(2018).

    [15] Ma Y J, Ma Y T, Chen J H. Vehicle recognition based on multi-layer features of convolutional neural network and support vector machine[J]. Laser & Optoelectronics Progress, 56, 141001(2019).

    [16] He K M, Zhang X Y, Ren S Q et al. Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. [C]∥2015 IEEE International Conference on Computer Vision (ICCV), December 7-13, 2015, Santiago, Chile. New York: IEEE, 1026-1034(2015).

    Jian Xu, Shupei Wu, Xiuping Liu. Classification of Bobbins Based on Improved Deep Neural Network[J]. Laser & Optoelectronics Progress, 2020, 57(10): 101014
    Download Citation