基于深度学习的表面缺陷检测方法综述

陶显; 侯伟; 徐德

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

陶显^1,,
侯伟^{1, 2,},
徐德^{1, 2,}

1.
中国科学院自动化研究所精密感知与控制研究中心北京 100190
2.
中国科学院大学人工智能学院北京 101408

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

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出版历程
- 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

A Survey of Surface Defect Detection Methods Based on Deep Learning

TAO Xian^1
,,
HOU Wei^{1, 2
,},
XU De^{1, 2
,}

1.
Research Center of Precision Sensing and Control, Institute of Automation, Chinese Academy of Sciences, Beijing 100190
2.
School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 101408

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

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.
- 深度学习 /
- 表面缺陷检测 /
- 机器视觉 /
- 卷积神经网络
Abstract:
In recent years, surface defect detection techniques based on deep learning have been widely used in various industrial scenarios. This paper reviews the latest works on deep learning based surface defect detection methods. They are classified into three categories: full-supervised learning model method, unsupervised learning model method and other methods. The typical methods are further subdivided and compared. The advantages and disadvantages of these methods and their application scenarios are summarized. This paper analyzes three key issues in surface defect detection and introduces common data sets for industrial surface defects. Finally, the future development trend of surface defect detection is predicted.
- Deep learning /
- surface defect detection /
- machine vision /
- convolutional neural network (CNN)

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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)

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图 2 缺陷检测的问题定义

Fig. 2 Definition of defect detection problem

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图 3 缺陷检测方法框架图

Fig. 3 Framework of defect detection methods

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图 4 基于滑动窗口的裂纹定位

Fig. 4 Crack location based on sliding window

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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

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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

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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)

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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
定位	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
无监督学习	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

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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	否	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	否	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	否	98.6	2017
Yu^[65]	两阶段 FCN 网络	是	95.9934	2017
Racki^[39]	多任务分类网络, 包括分类分支和分割分支	否	99.655	2018

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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

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