[1] Xu M F, Shi Y B, Gao W H et al. Surface roughness measurement of screen for laser projection display[J]. Chinese Journal of Lasers, 41, 108005(2014).
[2] Ye J H, Liu Y, Xu G C et al. Evaluation of surface quality of lap laser weld joints based on noncontact measurement[J]. Chinese Journal of Lasers, 46, 1002008(2019).
[3] Younis M A. On line surface roughness measurements using image processing towards an adaptive control[J]. Computers & Industrial Engineering, 35, 49-52(1998).
[4] Yi H A, Liu J, Lu E H. Detection method of grinding surface roughness based on image definition evaluation[J]. Journal of Mechanical Engineering, 52, 15-21(2016).
[5] Yi H A, Liu J, Ao P et al. Visual method for measuring the roughness of a grinding piece based on color indices[J]. Optics Express, 24, 17215-17233(2016).
[6] Ye H, Weng Z X, Zhang Y H et al. Surface roughness measurement using laser confocal microscope with boundary area correction[J]. Laser & Optoelectronics Progress, 57, 211203(2020).
[7] 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).
[8] 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).
[11] Sung F, Yang Y X, Zhang L et al. Learning to compare: relation network for few-shot learning[C], 1199-1208(2018).
[13] Liu Y, Lei Y B, Fan J L et al. Survey on image classification technology based on small sample learning[J]. Acta Automatica Sinica, 47, 297-315(2021).
[14] Shorten C, Khoshgoftaar T M. A survey on image data augmentation for deep learning[J]. Journal of Big Data, 6, 1-48(2019).
[15] Finn C, Abbeel P, Levine S. Model-agnostic meta-learning for fast adaptation of deep networks[C], 1126-1135(2017).
[16] Zhang H, Liu J, Chen S F et al. Novel roughness measurement for grinding surfaces using simulated data by transfer kernel learning[J]. Applied Soft Computing, 73, 508-519(2018).