• High Power Laser and Particle Beams
  • Vol. 34, Issue 11, 112002 (2022)
Yanhua Shao, Yupei Feng, Xiaoqiang Zhang, and Hongyu Chu*
Author Affiliations
  • School of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, China
  • show less
    DOI: 10.11884/HPLPB202234.220023 Cite this Article
    Yanhua Shao, Yupei Feng, Xiaoqiang Zhang, Hongyu Chu. Using deep learning for surface defects identification of optical components[J]. High Power Laser and Particle Beams, 2022, 34(11): 112002 Copy Citation Text show less
    References

    [1] Lin Zunqi. Progress of laser fusion[J]. Chinese Journal of Lasers, 37, 2202-2207(2010).

    [2] Chu Hongyu. Research on the detecting technology of surface optics defect f highpower laser device based on machine vision[D]. Chongqing: Chongqing University, 2011: 2142, 95106

    [3] Sowers I F. Optical cleanliness specifications cleanliness verification[C]Proceedings of the 44th Annual Meeting of the International Symposium on Optical Science, Engineering, Instrumentation. 1999: 525530.

    [4] Shi Wei. Research on high precision purification testing methods[D]. Chengdu: Sichuan University, 2000: 915

    [5] Fan Yong, Chen Niannian, Gao Lingling, . Digital detection system of surface defects for large aperture optical elements[J]. High Power Laser and Particle Beams, 21, 1032-1036(2009).

    [6] Wang Fengquan, Yang Yongying, Sun Dandan. Research of digital inspection system of precise surface defect[J]. Optical Instruments, 28, 71-75(2006).

    [7] Zhu Xiaolong, Wang Yi, Xie Zhijiang, . Surface cleanliness level detection by imaging method for precision optical elements[J]. Journal of Southwest Jiaotong University, 44, 958-962(2009).

    [8] Fu Xiangwen. Research of the key technology on detection system of lens defect based on image recognition[D]. Shenyang: Shenyang Ligong University, 2014: 5361

    [9] Feng Bo. Research on final optics damage online inspection technologies f ICF system[D]. Harbin: Harbin Institute of Technology, 2014: 6382

    [10] He Xiaosong, Zhang Zhanwen, Rong Weibin. Detection and classification of microspheres based on computer vision[J]. High Power Laser and Particle Beams, 29, 084102(2017).

    [11] Zhang Wenxue, Wang Jihong, Ren Ge. Optical elements defect online detection method based on camera array[J]. High Power Laser and Particle Beams, 32, 051001(2020).

    [12] Xiong Xianming, Zhang Qiankun, Qin Zujun. Research on highway state detection based on visible-near-infrared spectrum[J]. Infrared Technology, 43, 131-137(2021).

    [13] Nan Zhefeng. Research on track defect detection algithm based on machine vision[D]. Lanzhou: Lanzhou Jiaotong University, 2021: 3848

    [14] Lecun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 86, 2278-2324(1998).

    [15] Chollet F. Deep learning with python[M]. 2nd ed. New Yk: Manning, 2021: 2026, 202221.

    [16] Zhang A, Li Mu, Lipton Z C, et al. Dive into deep learning[M]. Beijing: Posts & Telecom Press, 2019: 6265

    [17] Huang Jun, Zhang Nana, Zhang Hui. Silent live face detection in near-infrared images based on optimized LeNet-5[J]. Infrared Technology, 43, 845-851(2021).

    CLP Journals

    [1] Shibiao Liao, Tao Luo, Runheng Xiao, Junjie Cheng, Chang Shu, Haiqing Li, Yingbin Xing, Nengli Dai, Jinyan Li. Breakthrough of 4 kW narrow linewidth linearly polarized laser based on a fiber oscillator laser and a homemade Yb-doped fiber[J]. High Power Laser and Particle Beams, 2023, 35(9): 091004

    Yanhua Shao, Yupei Feng, Xiaoqiang Zhang, Hongyu Chu. Using deep learning for surface defects identification of optical components[J]. High Power Laser and Particle Beams, 2022, 34(11): 112002
    Download Citation