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基于卷积神经网络的光学元件表面缺陷图像分类

侯劲尧 刘卫国 周顺 高爱华 葛少博 肖相国

侯劲尧, 刘卫国, 周顺, 高爱华, 葛少博, 肖相国. 基于卷积神经网络的光学元件表面缺陷图像分类[J]. 应用光学.
引用本文: 侯劲尧, 刘卫国, 周顺, 高爱华, 葛少博, 肖相国. 基于卷积神经网络的光学元件表面缺陷图像分类[J]. 应用光学.
HOU Jinyao, LIU Weiguo, ZHOU Shun, GAO Aihua, GE Shaobo, XIAO Xiangguo. Surface detection iamge of optical element surface defects based on convolutional neural network[J]. Journal of Applied Optics.
Citation: HOU Jinyao, LIU Weiguo, ZHOU Shun, GAO Aihua, GE Shaobo, XIAO Xiangguo. Surface detection iamge of optical element surface defects based on convolutional neural network[J]. Journal of Applied Optics.

基于卷积神经网络的光学元件表面缺陷图像分类

基金项目: 基金项目:陕西省科技厅重点研发项目资助(2019GY-063)
详细信息
    作者简介:

    侯劲尧(1995—),男,博士研究生,主要从事光电检测和图像处理方面的研究。E-mail:894608824@qq.com

    通讯作者:

    刘卫国(1964—),男,教授,博士生导师,主要从事光电子技术、电子材料技术研究。E-mail:wgliu@163.com

  • 中图分类号: TP751

Surface detection iamge of optical element surface defects based on convolutional neural network

  • 摘要: 光学元件的表面疵病,即表面缺陷,其形状的大小会直接影响光学系统的性能,在对表面缺陷进行分类时,所面对的很多表面缺陷的形状都是不规则的,依靠普通的模式识别技术,分类很难达到预期的效果。为解决精密光学元件表面缺陷分类方法中精度低、耗时长的问题,提出了基于卷积神经网络的精密光学元件表面缺陷分类方法。采用散射法获取表面缺陷图像分析其成像特点,通过对图像进行旋转,镜像扩增了数据集,加强了网络的训练能力。使用AC训练网络模型,在不增加额外计算量的同时加强了网络的特征获取力。最后,通过Softmax分类器,将精密光学元件表面缺陷分为划痕、麻点及噪点3类。实验结果表明,所使用的模型对缺陷分类精度超过99.05%。
  • 图  1  表面缺陷图像

    Fig.  1  Surface defect image

    图  2  表面缺陷MER模型

    Fig.  2  MER model of surface defects

    图  3  光学元件表面缺陷图像分类流程图

    Fig.  3  Image classification flow chart of surface defects of optical components

    图  4  表面缺陷图像数据集的建立

    Fig.  4  Establishment of surface defect image data set

    图  5  AlexNet网络模型

    Fig.  5  AlexNet network model

    图  6  非对称卷积过程

    Fig.  6  Asymmetric convolution process

    图  7  本文网络结构

    Fig.  7  Network structure of this paper

    表  1  表面缺陷特征提取

    Table  1  Extraction of surface defect features

    表面缺陷长轴/
    pixel
    短轴/
    pixel
    面积/
    pixel2
    外接矩形
    面积/pixel2
    长宽比矩形度P
    1114114
    2114114
    11318332.67
    12228412
    13321061.51.67
    168648501.330.96
    179856721.130.78
    1811.267.736987.071.460.79
    1910.299.8478101.201.050.77
    2032.6531.7588104.281.030.84
    2687.7428.8713762 532.953.10.54
    下载: 导出CSV

    表  2  改进前后网络模型参数量的对比

    Table  2  Comparison of the number of network model parameters before and after improvement

    模型模型总参数
    Alexnet58 271 811
    Alexnet+AC54 405 027
    下载: 导出CSV

    表  3  本文网络识别结果

    Table  3  Network detection results in this paper %

    表面缺陷ScratchDigNoisy
    准确率10099.7597.4
    下载: 导出CSV

    表  4  模型对比实验

    Table  4  Model comparison experiments

    Model灵敏度/%特异性/%精确性/%运行时间/s
    VGG1669.3991.2096.4081.1
    InceptionV371.7492.2997.1279.35
    ResNet5073.8794.1797.3278.56
    AlexNet77.2092.3497.2018.54
    Ours89.9796.7399.0518.46
    下载: 导出CSV
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  • 网络出版日期:  2022-11-17

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