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基于深度学习的表面缺陷检测方法综述

陶显 侯伟 徐德

陶显, 侯伟, 徐德. 基于深度学习的表面缺陷检测方法综述. 自动化学报, 2021, 47(5): 1017−1034 doi: 10.16383/j.aas.c190811
引用本文: 陶显, 侯伟, 徐德. 基于深度学习的表面缺陷检测方法综述. 自动化学报, 2021, 47(5): 1017−1034 doi: 10.16383/j.aas.c190811
Tao Xian, Hou Wei, Xu De. A survey of surface defect detection methods based on deep learning. Acta Automatica Sinica, 2021, 47(5): 1017−1034 doi: 10.16383/j.aas.c190811
Citation: Tao Xian, Hou Wei, Xu De. A survey of surface defect detection methods based on deep learning. Acta Automatica Sinica, 2021, 47(5): 1017−1034 doi: 10.16383/j.aas.c190811

基于深度学习的表面缺陷检测方法综述

doi: 10.16383/j.aas.c190811
基金项目: 国家自然科学基金(61703399, 61703398, 61973302, 61673383)资助
详细信息
    作者简介:

    陶显:中国科学院自动化研究所副研究员. 2016年获得中国科学院自动化研究所博士学位. 主要研究方向为机器视觉, 缺陷检测和深度学习. 本文通信作者.E-mail: taoxian2013@ia.ac.cn

    侯伟:中国科学院大学人工智能学院博士研究生. 2009年和2014年分别获得兰州大学学士和硕士学位. 主要研究方向为缺陷检测, 计算机视觉, 图像处理和机器学习.E-mail: houwei2018@ia.ac.cn

    徐德:中国科学院自动化研究所研究员. 1985年和1990年分别获得山东工业大学学士和硕士学位. 2001年获得浙江大学博士学位. 主要研究方向为机器人视觉测量, 视觉控制, 智能控制, 视觉定位, 显微视觉, 微装配.E-mail: de.xu@ia.ac.cn

A Survey of Surface Defect Detection Methods Based on Deep Learning

Funds: Supported by National Natural Science Foundation of China (61703399, 61703398, 61973302, 61673383)
More Information
    Author Bio:

    TAO Xian Associate professor at the Institute of Automation, Chinese Academy of Sciences. He received his Ph.D. degree at the Institute of Automation, Chinese Academy of Sciences in 2016. His research interest covers machine vision, defect detection, and deep learning. Corresponding author of this paper

    HOU Wei Ph.D. candidate at the School of Artificial Intelligence, University of Chinese Academy of Science. He received his bachelor degree and master degree from Lanzhou University in 2009 and 2014, respectively. His research interest covers defect detection, computer vision, image processing, and machine learning

    XU De Professor at the Institute of Automation, Chinese Academy of Sciences. He received his bachelor degree and master degree from Shandong University of Technology in 1985 and 1990, respectively, and received his Ph.D. degree from Zhejiang University in 2001. His research interest covers robotics and automation such as visual measurement, visual control, intelligent control, visual positioning, microscopic vision, and microassembly

  • 摘要:

    近年来, 基于深度学习的表面缺陷检测技术广泛应用在各种工业场景中. 本文对近年来基于深度学习的表面缺陷检测方法进行了梳理, 根据数据标签的不同将其分为全监督学习模型方法、无监督学习模型方法和其他方法三大类, 并对各种典型方法进一步细分归类和对比分析, 总结了每种方法的优缺点和应用场景. 本文探讨了表面缺陷检测中三个关键问题, 介绍了工业表面缺陷常用数据集. 最后, 对表面缺陷检测的未来发展趋势进行了展望.

  • 图  1  复杂工业环境下的表面缺陷图像

    Fig.  1  Images of surface defects in complex industrial environment ((a) Scratch image of dark field image of optical component; (b) Surface defect of building bridge; (c) Strip surface defect; (d) Unmanned aerial vehicle insulator defect)

    图  2  缺陷检测的问题定义

    Fig.  2  Definition of defect detection problem

    图  3  缺陷检测方法框架图

    Fig.  3  Framework of defect detection methods

    图  4  基于滑动窗口的裂纹定位

    Fig.  4  Crack location based on sliding window

    表  1  商用基于深度学习的缺陷检测软件

    Table  1  Commercial deep learning based defect detection software

    软件名称 公司 国家 年份
    VIDI 已被康耐视收购 瑞士 2016
    Halcon17 以上版本 MVTec 德国 2017
    SuaKIT 数优−AI 深度
    学习缺陷检测软件
    已被康耐视收购 韩国 2017
    ALFA 深度学习外观
    缺陷检测软件
    东莞埃法智能科技有限公司 中国 2018
    AiDitron 人工智能软件 杭州谛创科技有限公司 中国 2018
    下载: 导出CSV

    表  2  分类网络各子方法优缺点对比

    Table  2  Comparison of advantages and disadvantages of each sub-method of classification network

    代表子方法 优点 缺点
    直接分类 结构经典, 也是其他分类网络子方法
    的基础, 可参考诸多现成网络
    缺陷在图像中需要占一定比例, 否则其特征容易被池化掉, 同时一般
    一幅图像中只容许存在一种类别的缺陷 (多标签分类除外)
    定位 ROI 后分类 获取 ROI 的缺陷信息 需借助其他方法获取 ROI
    多类别分类 一定程度上解决样本不平衡问题 网络采用二级训练
    滑动窗口 在大图中实现缺陷的粗定位 滑动窗口尺寸需要准确选择, 且只能获得较粗位置, 遍历滑动速度慢
    热力图 得到较为精准的缺陷区域 缺陷精确定位效果依赖网络分类性能
    多任务学习 联合其他网络同时获取缺陷精确位置
    和类别, 也能减少所需训练样本数目
    网络结构相对复杂, 在添加分割分支时, 需要逐像素的标签
    做特征提取器 获取有效的缺陷特征 依赖其他分类器才能获得最终分类结果
    下载: 导出CSV

    表  3  传统图像处理与基于深度学习的缺陷检测方法的比较

    Table  3  Comparison between traditional image processing and deep learning-based defect detection methods

    对比项目 传统基于图像处理的方法 深度学习方法
    方法 1) 结构法: 边缘、骨架、形态学等 基于卷积神经网络 CNN
    2) 统计法: 直方图、局部二值化特征 LBP、纹理特征、灰度共生矩阵 GLCM 等
    3) 滤波法: 空间滤波、频域滤波 (傅里叶、gabor、小波) 等
    4) 模型法: 随机场模型、反散射模型和分形体等
    本质 人工设计特征 + 分类器 (或规则) 从大量数据中自动学习特征
    所需条件 相对苛刻的成像环境要求, 缺陷和非缺陷区域之间的高对比度, 少噪声 足够的学习数据和高性能运算单元
    适应性 差 (成像环境变化或缺陷类型变化时往往需要更改阈值或重新设计算法) 相对强 (能够应对一定的工业检测环境变化)
    下载: 导出CSV

    表  4  工业表面缺陷检测常用数据集

    Table  4  Common data sets for industrial surface defect detection

    方法 应用场景 数据集名称 链接
    分类 钢材表面 NEU-CLS[51] http://faculty.neu.edu.cn/yunhyan/NEU_surface_defect_database.html
    太阳能板 elpv-dataset[109] https://github.com/zae-bayern/elpv-dataset
    金属表面 KolektorSDD[40] http://www.vicos.si/Downloads/KolektorSDD
    木材表面 wood defect database[137] http://www.ee.oulu.fi/olli/Projects/Lumber.Grading.html
    定位 钢材表面 NEU-DET[51] http://faculty.neu.edu.cn/yunhyan/NEU_surface_defect_database.html
    铸件X射线图像 GDXray Casting[138] https://domingomery.ing.puc.cl/material/gdxray/
    分割 磁瓦表面 Magnetic-tile-defect-datasets[67] https://github.com/abin24/Magnetic-tile-defect-datasets.
    钢轨表面 RSDDs dataset[139] http://icn.bjtu.edu.cn/Visint/resources/RSDDs.aspx
    地面裂纹 Crack_Dataset[140] https://drive.google.com/drive/folders/1cplcUBmgHfD82YQTWnn1dssK2Z_xRpjx
    桥梁裂缝 Bridge Cracks[141] https://github.com/maweifei/BridgeCrack_Image_Data
    孪生网络 PCB 板 PCB Dataset[90] https://github.com/tangsanli5201/DeepPCB
    无监督学习 多种材质缺陷 MVTec AD[142] http://www.mvtec.com/company/research/datasets
    扫描隧道显微镜成像
    SEM 材料表面
    NanoTWICE[143] http://www.mi.imati.cnr.it/ettore/NanoTWICE/
    弱监督学习 纹理缺陷 DAGM 2007[144] https://hci.iwr.uni-heidelberg.de/node/3616
    下载: 导出CSV

    表  5  典型缺陷分类方法在DAGM数据集上性能比较

    Table  5  Effect comparison of defect classification methods on DAGM

    方法 基础网络描述 是否预训练 分类准确率 (%) 发表时间
    Weimer[145] 滑动窗口分类网络, 窗口大小 32×32 99.2 2016
    Wang[146] 基于滑动窗口和多类别分类相结合的网络,
    128×128大小的滑动窗口, 搭建的11层 CNN 网络
    99.8 2017
    Wu[147] 基于多尺度滑动窗口和分类网络, 8 层网络, 包括 inception 结构 98.6 2017
    Yu[65] 两阶段 FCN 网络 95.9934 2017
    Racki[39] 多任务分类网络, 包括分类分支和分割分支 99.655 2018
    下载: 导出CSV

    表  6  典型缺陷定位方法在GDXray casting数据集上性能比较

    Table  6  Effect comparison of defect location methods on GDXray casting

    方法 mAP 使用GPU评估的
    时间/单幅图像 (s)
    滑动窗口 0.461 0.231
    Faster R-CNN (VGG-16)[43] 0.865 0.438
    Faster R-CNN (ResNet-101)[43] 0.921 0.512
    R-FCN (ResNet-101)[148] 0.875 0.375
    SSD (VGG-16)[44] 0.697 0.025
    SSD (ResNet-101)[44] 0.762 0.051
    下载: 导出CSV
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  • 收稿日期:  2019-11-27
  • 录用日期:  2020-03-25
  • 网络出版日期:  2021-03-04
  • 刊出日期:  2021-05-20

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