• Laser & Optoelectronics Progress
  • Vol. 58, Issue 20, 2015001 (2021)
Meng Qi1, Miao Hua1、*, Li Lin1, Guo Bo1, Liu Tingting1, and Mi Shilong2
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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    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.
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    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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    Category: Machine Vision
    Received: Nov. 28, 2020
    Accepted: Jan. 2, 2021
    Published Online: Oct. 14, 2021
    The Author Email: Miao Hua (ilev24@163.com)