• Optical Instruments
  • Vol. 45, Issue 4, 17 (2023)
Qiang ZHANG, Zhiwen HUANG, and Jianmin ZHU*
Author Affiliations
  • School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
  • show less
    DOI: 10.3969/j.issn.1005-5630.2023.004.003 Cite this Article
    Qiang ZHANG, Zhiwen HUANG, Jianmin ZHU. Design of roughness detection system based on transfer learning and model fusion[J]. Optical Instruments, 2023, 45(4): 17 Copy Citation Text show less
    References

    [3] AL-KINDI G A, SHIRINZADEH B. Feasibility assessment of vision-based surface roughness parameters acquisition for different types of machined specimens[J]. Image and Vision Computing, 27, 444-458(2009).

    [4] WHITEHOUSE D J. Stylus contact method for surface metrology in the ascendancy[J]. Measurement and Control, 31, 48-50(1998).

    [10] CHEN Y L, YI H A, LIAO C, et al. Visual measurement of milling surface roughness based on Xception model with convolutional neural network[J]. Measurement, 186, 110217(2021).

    [11] RIFAI A P, AOYAMA H, THO N H, et al. Evaluation of turned and milled surfaces roughness using convolutional neural network[J]. Measurement, 161, 107860(2020).

    [13] SIMONYAN K, ZISSERMAN A. Very deep convolutional wks f largescale image recognition[C]3rd International Conference on Learning Representations. San Diego: ICLR, 2015.

    [14] SZEGEDY C, VANHOUCKE V, IOFFE S, et al. Rethinking the inception architecture f computer vision[C]2016 IEEE Conference on Computer Vision Pattern Recognition (CVPR). Las Vegas: IEEE, 2016: 2818 2826.

    [15] HUANG G, LIU Z, VAN DER MAATEN L, et al. Densely connected convolutional wks[C]2017 IEEE Conference on Computer Vision Pattern Recognition. Honolulu: IEEE, 2017: 2261 2269.

    Qiang ZHANG, Zhiwen HUANG, Jianmin ZHU. Design of roughness detection system based on transfer learning and model fusion[J]. Optical Instruments, 2023, 45(4): 17
    Download Citation