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纹理表面缺陷机器视觉检测方法综述

朱贺 杨华 尹周平

朱贺,杨华,尹周平. 纹理表面缺陷机器视觉检测方法综述[J]. 机械科学与技术,2023,42(8):1293-1315 doi: 10.13433/j.cnki.1003-8728.20220086
引用本文: 朱贺,杨华,尹周平. 纹理表面缺陷机器视觉检测方法综述[J]. 机械科学与技术,2023,42(8):1293-1315 doi: 10.13433/j.cnki.1003-8728.20220086
ZHU He, YANG Hua, YIN Zhouping. Review of Machine Vision Detection Methods for Texture Surface Defects[J]. Mechanical Science and Technology for Aerospace Engineering, 2023, 42(8): 1293-1315. doi: 10.13433/j.cnki.1003-8728.20220086
Citation: ZHU He, YANG Hua, YIN Zhouping. Review of Machine Vision Detection Methods for Texture Surface Defects[J]. Mechanical Science and Technology for Aerospace Engineering, 2023, 42(8): 1293-1315. doi: 10.13433/j.cnki.1003-8728.20220086

纹理表面缺陷机器视觉检测方法综述

doi: 10.13433/j.cnki.1003-8728.20220086
基金项目: 国家自然科学基金项目(51875228)、国家重点研发计划(2020YFA0405700)及佛山市产业领域科技攻关专项(2020001006509)
详细信息
    作者简介:

    朱贺(1997−),硕士研究生,研究方向为高速机器视觉、基于机器视觉的纹理表面缺陷检测,zhuhe@hust.edu.cn

    通讯作者:

    杨华,教授,博士生导师,huayang@hust.edu.cn

  • 中图分类号: TP399

Review of Machine Vision Detection Methods for Texture Surface Defects

  • 摘要: 纹理表面缺陷检测在机器视觉领域具有意义和挑战性,其历史可以追溯到20世纪中后期,近年来随着深度学习技术的蓬勃发展,纹理表面缺陷检测技术大幅飞跃。直至今日,关于纹理表面缺陷检测的调研和综述仍然很少。在此背景下,本文回顾2017年-2021年间200余篇纹理表面缺陷机器视觉检测论文,对纹理表面缺陷机器视觉检测研究进展进行了及时、全面的调查;分析了纹理表面缺陷检测的发展历史和最新研究进展,原则上将纹理表面缺陷机器视觉检测方法分为传统方法与深度学习方法,并对二者进行了深层次研究分析,特别是深度学习方法;对近期出现的几种纹理表面缺陷机器视觉检测方法主题进行总结的同时,也对这些主题的研究进展进行了综述。最后,对未来的研究趋势进行了展望,以期为后续研究提供指导和启示。
  • 图  1  纹理表面缺陷示例

    Figure  1.  Examples of texture surface defects

    图  2  纹理表面缺陷视觉检测方法分类框架图

    Figure  2.  Classification framework of visual detection methods for texture surface defects

    图  3  傅里叶变换方法[51]

    Figure  3.  The Fourier transform method[51]

    图  4  基于SVM缺陷检测方法[34]

    Figure  4.  The SVM-based defect detection method[34]

    图  5  用于缺陷检测的传统CNN网络框架[76]

    Figure  5.  The traditional CNN network framework for defect detection[76]

    图  6  基于目标检测方法缺陷检测[95]

    Figure  6.  The object detection-based defect detection method[95]

    图  7  基于FCN网络缺陷检测[102]

    Figure  7.  FCN network-based defect detection[102]

    图  8  基于GAN的无监测缺陷检测AnoGAN[137]

    Figure  8.  AnoGAN-based unsupervised defect detection using GAN[137]

    图  9  基于CycleGAN的缺陷检测[167]

    Figure  9.  Defect detection using CycleGAN[167]

    图  10  基于序列模型缺陷检测[209]

    Figure  10.  Defect detection using sequence models[209]

    表  1  分类结果混淆矩阵

    Table  1.   Confusion matrix for the classification results

    真实情况预测结果
    正例反例
    正例PTNF
    反例PFNT
    下载: 导出CSV

    表  2  DAGM_2007[77]数据集测试效果

    Table  2.   Test results using the DAGM_2007[77] dataset

    方法方法描述F1指标/%RAccuracy%方法分类发表年份
    Yu等[100]基于两阶段全卷积FCN网络的表面缺陷检测方法95.99基于语义分割方法2017
    Wang等[76]基于11层卷积神经网络的表面缺陷检测方法99.80基于传统CNN方法2018
    Zhou等[175]基于双VGG16网络的表面缺陷检测方法99.49迁移学习方法2019
    Enshaei等[115]使用粗略标记的弱监督U-net表面缺陷检测方法79.3399.16弱监督学习方法2020
    Chen等[118]基于注意力结构卷积神经网络的纹理表面缺陷检测方法74.4699.85基于弱监督学习方法2020
    Zhang等[216]基于弱监督学习的分类感知对象检测方法65.887.60基于知识蒸馏2021
    Tsai等[152]基于改进的CAE模型的纹理表面缺陷检测方法95.00基于无缺陷样本训练方法2021
    下载: 导出CSV

    表  3  MVTec[151]数据集测试效果

    Table  3.   Test results using the MVTec[151] dataset

    方法方法描述RAccuracy%方法分类发表年份
    Schlegl等[137] 基于GAN的缺陷检测方法AnoGAN 55 基于无缺陷样本
    训练方法
    2017
    Bergmann等[154] 基于结构相似性感知损失SSIM卷积自编码器AE网络的表面缺陷检测方法 63 基于无缺陷样本
    训练方法
    2019
    Venkataramanan等[156] 基于引导注意对抗变分自编码器GAVGA的缺陷检测方法(无监督) 78 基于无缺陷样本
    训练方法
    2020
    Wang等[150] 基于VQ-VAE的表面缺陷检测方法 85 基于无缺陷样本
    训练方法
    2020
    Tsai等[152] 基于改进CAE模型的纹理表面缺陷检测方法 91 基于无缺陷样本
    训练方法
    2021
    Tellaeche等[148] 基于卷积自编码器与OC-SVM的纹理表面缺陷检测方法 92 基于无缺陷样本
    训练方法
    2021
    下载: 导出CSV

    表  4  纹理表面缺陷机器视觉检测方法优缺点比较

    Table  4.   Comparison of the pros and cons of machine vision detection methods for texture surface defects

    分类方法优点缺点
    传统方法 图像结构方法 方法原理简单 多数方法泛化能力差,仅适用于某一种特定情况下的缺陷检测
    频域分析方法 在空间域较难分离的特征在频域可分离性提升 频域分析方法对噪声敏感,多数情况下计算复杂,耗时较长
    传统机器学习方法 缺陷检测效果优于其他传统方法;与深度学习方法相比,可解释性强 在实际使用过程中,超参数对检测效果影响巨大
    深度学习方法 监督学习方法 具有较高的缺陷检测精度,是目前缺陷检测效果最好的方法 需要大量标注正确的训练样本
    弱监督学习方法 基于存在标注问题样本进行训练,方法可达到较高精度 无过明显缺陷,属于监督学习与无监督学习方法的折中
    无监督学习方法 模型训练简单,仅使用无标记样本训练,不需要标注 缺陷检测精度相对监督学习方法较低
    迁移学习方法 使用其他领域数据训练模型,减少了对缺陷样本的需求 在不同预训练数据集下效果差异较大,存在负迁移、负适配问题
    主动学习方法 可以在样本标注较少情况下进行训练 模型训练需要人工与机器迭代,虽然减少了标注样本数量,但标注代价并没有过多下降
    下载: 导出CSV
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  • 收稿日期:  2021-11-02
  • 网络出版日期:  2023-09-13
  • 刊出日期:  2023-08-31

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