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基于优化残差网络的复杂纹理表面缺陷检测

林李兴 夏振平 徐浩 宋玉 胡伏原

林李兴, 夏振平, 徐浩, 宋玉, 胡伏原. 基于优化残差网络的复杂纹理表面缺陷检测[J]. 应用光学, 2023, 44(1): 104-112. doi: 10.5768/JAO202344.0102006
引用本文: 林李兴, 夏振平, 徐浩, 宋玉, 胡伏原. 基于优化残差网络的复杂纹理表面缺陷检测[J]. 应用光学, 2023, 44(1): 104-112. doi: 10.5768/JAO202344.0102006
LIN Lixing, XIA Zhenping, XU Hao, SONG Yu, HU Fuyuan. Defect detection on complex texture surface based on optimized ResNet[J]. Journal of Applied Optics, 2023, 44(1): 104-112. doi: 10.5768/JAO202344.0102006
Citation: LIN Lixing, XIA Zhenping, XU Hao, SONG Yu, HU Fuyuan. Defect detection on complex texture surface based on optimized ResNet[J]. Journal of Applied Optics, 2023, 44(1): 104-112. doi: 10.5768/JAO202344.0102006

基于优化残差网络的复杂纹理表面缺陷检测

doi: 10.5768/JAO202344.0102006
基金项目: 国家自然科学基金(62002254,61876121)江苏省自然科学基金(BK20200988)
详细信息
    作者简介:

    林李兴(1997—),男,硕士研究生,主要从事机器视觉检测方面的研究。E-mail:llxgongzuo@126.com

    通讯作者:

    夏振平(1985—),男,博士,副教授,主要从事信息显示、机器视觉、图像处理方面的研究。E-mail:xzp@usts.edu.cn

  • 中图分类号: TN27

Defect detection on complex texture surface based on optimized ResNet

  • 摘要: 产品表面缺陷检测是工业自动化生产的重要环节,准确率是评价自动检测系统可靠性的主要指标。基于复杂纹理表面缺陷检测的特殊性以及对检测方法的实时性、通用性等要求,提出了优化骨干网络并使用迁移学习特征映射构建复杂纹理表面缺陷的检测方法。该方法通过优化残差网络模型并建立仿真数据集的方式进行迁移学习,以解决实际情况中复杂纹理表面产品数据集样本数量少、数据集制作困难、相似问题难以互相兼容等问题。实验结果表明,提出的方法可以准确地检测随机复杂纹理的人造木质板材表面缺陷,平均准确率可达99.6%。现有实验条件下单张人造木质板材的检测时间为305 ms,可以满足在线检测的实时性要求。研究结果可为基于深度学习的复杂纹理表面缺陷检测提供新的思路与理论参考。
  • 图  1  迁移学习特征映射的方法示意图

    Fig.  1  Schematic diagram of method for transfer learning feature mapping

    图  2  原始图像经过预处理制作成数据集

    Fig.  2  Data set preprocessed by original images

    图  3  4组表面无缺陷与表面缺陷对比(红色虚线框标志部分为缺陷所在位置)

    Fig.  3  Comparison of four groups without and with surface defects, and red dotted line frame is location of defect

    图  4  仿真数据集构建方法示意图

    Fig.  4  Schematic diagram of construction method for simulation data set

    图  5  不同激活函数在测试数据集上的准确率和收敛比较

    Fig.  5  Comparison of accuracy and convergence of different activation functions on test data sets

    图  6  不同网络深度在测试数据集上的准确率和收敛比较

    Fig.  6  Comparison of accuracy and convergence of different network depths on test data sets

    图  7  改进ResNet18模型结构示意图

    Fig.  7  Structure diagram of improved ResNet18 model

    图  8  迁移学习方法

    Fig.  8  Schematic diagram of transfer learning method

    图  9  迁移学习方法的准确率验证

    Fig.  9  Accuracy verification of transfer learning method

    表  1  真实数据集参数

    Table  1  Parameters of real data set

    Batch No.Actual size/mSize/pixelCutting No.
    11.22×0.1753×7120×850350
    21.22×0.1753×7120×850300
    30.70×0.1753×7120×850300
    下载: 导出CSV

    表  2  实验环境和具体参数

    Table  2  Experimental environment and specific parameters

    Hardware Environment
    HardwareModel Number
    CPUInter core i7-10750H
    GPUNVIDIA RTX2060
    Memory24 GB
    Software Environment
    SoftwareName
    SystemWindows10
    configurationPytorch 3.6
    cuda 10.1
    Training parameters
    ParameterValue
    Batch size8
    Epoch25
    CUDAEnable
    下载: 导出CSV

    表  3  在模拟数据集上检测系统对四类缺陷的实验结果

    Table  3  Experimental results of four types of defects by detection system on simulated data sets

    Defect
    types
    Pollution
    defect
    Scratches
    pollution
    Breakage
    defect
    Lack of
    design and
    color
    Average
    Recall/%98.299.210010099.6
    Precision/%100100100100100
    Accuracy/%98.899.510010099.6
    Time/ms305305305305305
    下载: 导出CSV

    表  4  本文方法与其他方法在真实数据集上的结果对比

    Table  4  Results comparison of proposed method and other methods on real data sets

    ModelACCLOSS
    ResNet1883.2%0.332
    DenseNet12197.2%0.077
    SqueezeNet95.8%0.097
    MobileNet V392.0%0.118
    Our model98.7%0.011
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-03-28
  • 修回日期:  2022-07-14
  • 网络出版日期:  2022-11-17
  • 刊出日期:  2023-01-17

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