• Laser & Optoelectronics Progress
  • Vol. 58, Issue 20, 2015001 (2021)
Qi Meng1, Hua Miao1、*, Lin Li1, Bo Guo1, Tingting Liu1, and Shilong Mi2
Author Affiliations
  • 1School of Opto-Electronic Engineering, Changchun University of Science and Technology, Changchun, Jilin 130022, China
  • 2R&D Center, Dongguan Yutong Optical Technology Co.,Ltd, Dongguan, Guangdong 523841, China;
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    DOI: 10.3788/LOP202158.2015001 Cite this Article Set citation alerts
    Qi Meng, Hua Miao, Lin Li, Bo Guo, Tingting Liu, Shilong Mi. Data Enhancement of Lens Defect Based on Dual Channel Generative Adversarial Networks[J]. Laser & Optoelectronics Progress, 2021, 58(20): 2015001 Copy Citation Text show less

    Abstract

    Aiming at the problem of the low recognition rate of the deep learning defect detection algorithm under the condition of small samples, a data enhancement method based on two-channel generative adversarial network is proposed. The generative adversance network is composed of two channels, such as global discriminator and local discriminator. The local discriminator can increase the confidence loss of the defect type and realize the enhancement of local information. The proposed method is used to conduct experiments on the lens defect image dataset. Experimental results show that the nearest neighbor index, maximum mean difference, and Wasserstein distance of the proposed method are 0.52, 0.15 and 2.81, respectively. For the defect type images of pitting, scratches, bubbles and foreign bodies, the generated image quality is better than that of conditional generated adversarial network, Wasserstein distance generated adversarial network and Markov discriminator. The lens image generated by the dual-channel generation confrontation network has diverse global information and high-quality detailed features, which can effectively enhance the lens defect data set.
    Qi Meng, Hua Miao, Lin Li, Bo Guo, Tingting Liu, Shilong Mi. Data Enhancement of Lens Defect Based on Dual Channel Generative Adversarial Networks[J]. Laser & Optoelectronics Progress, 2021, 58(20): 2015001
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