• Laser & Optoelectronics Progress
  • Vol. 58, Issue 20, 2010015 (2021)
Ran Yan*, Jideng Liao, Xiaoyong Wu, Changjiang Xie, and Lei Xia
Author Affiliations
  • School of Mechanical Engineering, Chongqing University of Technology, Chongqing 400054, China
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    DOI: 10.3788/LOP202158.2010015 Cite this Article Set citation alerts
    Ran Yan, Jideng Liao, Xiaoyong Wu, Changjiang Xie, Lei Xia. Research on Classification Method of Sand and Gravel Aggregate Based on Convolutional Neural Network[J]. Laser & Optoelectronics Progress, 2021, 58(20): 2010015 Copy Citation Text show less
    References

    [1] Zhou X G, Qin X X, Lü Y L. Quality analysis of concrete aggregates and study on grading optimization[J]. Construction Quality, 34, 15-20(2016).

    [2] Rafael C G, Richard E W. Digital image processing[M]. 3rd ed. Ruan Q Q, Ruan Y Z, Transl(2010).

    [3] Liu G H. HALCON digital image processing[M](2018).

    [4] Fang H Y, Yang J H, Huang W J et al. A detection device and method for aggregate form quality: CN106969708A[P](2017).

    [5] Zeng B F, Yan Y P, Hu Z G. A method for measuring the mixing proportion of coarse and fine aggregate: CN106546980A[P](2017).

    [6] Yang J H, Li L N, Zhang R C et al. Fine aggregate on-line detection device and method: CN105699258A[P](2016).

    [7] Li X. Study on inspection method for aspect ratio of concrete coarse aggregate based on image processing[J]. Shanxi Architecture, 44, 103-105(2018).

    [8] Liu C, Xu Q, Shi B et al. Digital image recognition method of rock particle and pore system and its application[J]. Chinese Journal of Geotechnical Engineering, 40, 925-931(2018).

    [9] Zhang L, Yuan F N, Zhang W R et al. Review of fully convolutional neural network[J]. Computer Engineering and Applications, 56, 25-37(2020).

    [10] Yu S H. Development and application of convolution neural network[J]. Information & Communications, 32, 39-43(2019).

    [11] Yang Z Z, Kuang N, Fan L et al. Review of image classification algorithms based on convolutional neural networks[J]. Journal of Signal Processing, 34, 1474-1489(2018).

    [12] Xu B B, Cen K T, Huang J J et al. A survey on graph convolutional neural network[J]. Chinese Journal of Computers, 43, 755-780(2020).

    [13] Zhang T F, Zhong S C, Lian C M et al. Deep learning feature fusion-based retina image classification[J]. Laser & Optoelectronics Progress, 57, 241025(2020).

    [14] Su Z B, Gao M, Li P F et al. Digital printing defect classification algorithm based on convolutional neural network[J]. Laser & Optoelectronics Progress, 57, 241011(2020).

    [15] Jiang C, Hu A M, He W. Convolutional-neural-network based license plate location algorithm[J]. Laser & Optoelectronics Progress, 57, 021010(2020).

    [16] Liang L Y, Zhang T T, He W. Head pose estimation based on multi-scale convolutional neural network[J]. Laser & Optoelectronics Progress, 56, 131003(2019).

    [17] Zhang Y J, Wang Z L. Surface flaw detection of industrial products based on convolutional neural network[J]. IOP Conference Series: Earth and Environmental Science, 252, 022114(2019).

    [18] Ye H J, Han H, Zhu L N et al. Vegetable pest image recognition method based on improved VGG convolution neural network[J]. Journal of Physics: Conference Series, 1237, 032018(2019).

    [19] Hsu C Y, Chien J C. Ensemble convolutional neural networks with weighted majority for wafer bin map pattern classification[J]. Journal of Intelligent Manufacturing, 320, 2(2020).

    [20] Wang X W, Yin S L, Li H et al. A network intrusion detection method based on deep multi-scale convolutional neural network[J]. International Journal of Wireless Information Networks, 27, 503-517(2020).

    [21] Cheng G J, Guo W H, Fan P Z. Study on rock image classification based on convolution neural network[J]. Journal of Xi’an Shiyou University (Natural Science Edition), 32, 116-122(2017).

    [22] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[EB/OL]. (2015-04-10)[2020-07-25]. https: //arxiv.org/abs/1409.1556

    Ran Yan, Jideng Liao, Xiaoyong Wu, Changjiang Xie, Lei Xia. Research on Classification Method of Sand and Gravel Aggregate Based on Convolutional Neural Network[J]. Laser & Optoelectronics Progress, 2021, 58(20): 2010015
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