基于计算机视觉的工业金属表面缺陷检测综述

伍麟; 郝鸿宇; 宋友

doi:10.16383/j.aas.c230039

基于计算机视觉的工业金属表面缺陷检测综述

doi: 10.16383/j.aas.c230039

伍麟^1,,
郝鸿宇^1,,
宋友^1,

1.
北京航空航天大学北京 100083

基金项目: 河北省重点研发计划 (21310101D)资助

详细信息

作者简介:
伍麟：北京航空航天大学硕士研究生. 主要研究方向为计算机视觉, 目标检测和表面缺陷检测. E-mail: zf2021349@buaa.edu.cn

郝鸿宇：北京航空航天大学硕士研究生. 主要研究方向为计算机视觉, 图神经网络和少样本学习. E-mail: JoeyHao@buaa.edu.cn

宋友：北京航空航天大学教授. 主要研究方向为软件工程, 异常信号检测, 算法分析与设计. 本文通信作者. E-mail: songyou@buaa.edu.cn

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出版历程
- 收稿日期: 2023-02-06
- 录用日期: 2023-05-18
- 网络出版日期: 2023-07-03

A Review of Metal Surface Defect Detection Based on Computer Vision

WU Lin^1
,,
HAO Hong-Yu^1
,,
SONG You^1
,

1.
Beihang University, Beijing 100083

Funds: Supported by Key Research & Development Plan of Hebei Province (21310101D)

More Information

Author Bio:
WU Lin　Master student at the Beihang University. His research interest covers computer vision, object detection and surface defect detection

HAO Hong-Yu　Master student at the Beihang University. His research interest covers computer vision, graph neural network and few-shot learning

SONG You　Professor at School of Software, Beihang University. His research interest covers software engineering, anomaly signal detection, algorithm analysis and design. Corresponding author of this paper

摘要

摘要: 针对金属平面及三维结构材料的工业表面缺陷检测, 本文概述了视觉检测技术的基本原理和研究现状, 并总结出视觉自动检测系统的关键技术包括光学成像技术、图像预处理技术与缺陷检测器. 本文首先介绍了如何根据检测对象的光学特性选择合适的二维、三维光学成像技术; 其次介绍了图像降噪、特征提取、图像分割和拼接等预处理技术的重要作用; 然后根据缺陷检测器的实现原理将其分为模板匹配、图像分类、图像语义分割、目标检测和图像异常检测五类, 并对其中的经典算法进行了归纳分析. 最后, 本文探讨了工业场景下视觉检测技术实施中的关键问题, 并对该技术的发展趋势进行了展望.
- 表面缺陷检测 /
- 计算机视觉 /
- 金属表面缺陷 /
- 自动化检测
Abstract: Focusing on the industrial surface defect detection of metal planar and three-dimensional structural materials, this paper summarizes the basic principle and research status of visual defect detection technology, and summarizes the key technologies of visual automatic detection system including optical imaging technology, image preprocessing technology and defect detector. Firstly, this paper introduces how to select suitable 2D and 3D optical imaging technology according to the optical characteristics of the test object. Secondly, the important functions of image denoising, feature extraction, image segmentation and image Mosaic are introduced. Then, according to the implementation principle of defect detector, it is divided into five categories: template matching, image classification, image semantic segmentation, target detection and image anomaly detection, and the classical algorithms are summarized and analyzed. Finally, this paper discusses the key problems in the implementation of visual inspection technology in the industrial scene, and looks forward to the development trend of this technology.
- Surface anomaly detection /
- computer vision /
- metal surface defect /
- automatic inspection system

HTML全文

图 1 金属表面缺陷检测基本流程

Fig. 1 Pipline of metal surface defect detection

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图 2 自动光学成像系统^[2]

Fig. 2 Automated Optical Inspection system^[2]

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图 3 表面反射模型^[14]

Fig. 3 Light scattering model on surface^[14]

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图 4 照明光路类型

Fig. 4 Types of lighting path

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图 5 2D成像与3D成像对比

Fig. 5 2D imaging versus 3D imaging

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图 6 光度立体法^[23]

Fig. 6 Arrangement of photometric stereo measurement^[23]

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图 7 结构光法^[28]

Fig. 7 Schematic diagram of structured light illumination^[28]

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图 8 混合成像技术 (a ~ c) 2D灰度图像 (d ~ f)叠加3D深度信息的图像

Fig. 8 Hybrid imaging technique (a ~ c) 2D grayscale images (d ~ f) Images with 3D depthinformation represented

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图 9 基于图像分割的缺陷检测^[62]

Fig. 9 Defect detection based on image segmentation^[62]

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图 10 三元网络结构^[91]

Fig. 10 The structure of Triplet Network^[91]

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图 11 基于二阶段网络的金属表面缺陷检测^[100]

Fig. 11 Metal surface defect detection based on two-stage networks^[100]

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图 12 二阶段网络和一阶段网络对比

Fig. 12 Comparison of two-stage and one-stage networks

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图 13 DETR网络结构^[118]

Fig. 13 DETR: Object Detection with Transformer

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图 14 基于图像重建的缺陷检测(a)变分自编码机(b) GAN结合AE(c)基于记忆池的重构模型

Fig. 14 Defect detection based on image reconstruction (a) VEA (b) GAN associated with Auto-Encoder (c) Memory based model

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图 15 基于标准化流的缺陷检测(a)原始图像(b)多尺度输入(c)图像特征分布(d)简单分布(e)标准分布(f)异常分布

Fig. 15 Defect detection based on Normalizing Flow (a) Origin image (b) Multiscale input (c) Feature distribution (d) Simple distribution (e) Normalized distribution (f) Anomalous distribution

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图 16 (a)基于教师-学生网络的方法(b)基于最典型嵌入表示的方法

Fig. 16 (a) Method based on teacher-student network (b) Method based on the most typical embedding distribution

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表 1 目标检测算法在NEU-DET上的表现

Table 1 DEFECT DETECTION ON NEU-DET DATASET

Method	Backbone	Neck	$AP_{50}$
Faster R-CNN^[97]	ResNet-50	FPN	74.7
Cascade R-CNN^[99]	ResNet-50	FPN	75.8
YOLOX^[115]	CSPDarknet	PA-FPN	70.9
YOLOv4^[105]	CSPDarknet	FPN	76.4
AutoAssign^[116]	ResNet-50	FPN	76.6
AutoAssign^[116]	Swin-Tiny	FPN	78.3
DDN^[108]	ResNet-50	MFN	82.3
CA-AutoAssign^[117]	CSPDarknet	CA	82.7

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表 2 异常检测方法对比

Table 2 comparison of anormaly detection

Method	Detection AUROC	Segmentation AUROC	FPS
PatchCore Large^[145]	99.6	98.2	5.9
PNI^[146]	99.5	99.0	−
MemSeg^[144]	99.5	99.6	31.3
Fastflow^[141]	99.4	98.5	21.8
EfficientAD-M^[148]	99.1	96.9	269.0
EfficientAD-S^[148]	98.8	96.8	614.0
CS-Flow^[140]	98.7	−	−
Patch SVDD^[142]	92.1	95.7	2.1
VAE-Grad^[136]	89.2	−	−

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表 3 缺陷检测方法对比

Table 3 comparison of defect detection methods

方法	基本原理	应用场景	优缺点
模板匹配	比较模板与待检样本的差异来判断是否存在缺陷	产品高度一致的金属精密加工制成品, 例如手机外壳、汽车零件等	方法简单有效, 但需要提取制作模板, 仅适用于一致性强的产品
分类网络	直接用CNN网络提取特征, 通过Softmax或距离度量来预测类别	公差较大、尺寸较小的金属制品, 例如螺母、金属盖等零件	结构简单, 是其他网络的基础, 准确率依赖缺陷样本数量, 难以定位缺陷位置
目标检测	对每个提议候选框或者每个网格进行密集预测, 从背景中找出所有目标的分类和位置	适用于绝大多数缺陷类别可事先定义的工业场景, 技术最成熟	速度快, 适用范围广, 但网络结构复杂, 依赖大量缺陷样本进行训练
语义分割	通过卷积提取高阶语义特征, 然后通过上采样输出像素级的缺陷边界划分	大面积金属板、带制品, 缺陷具有成片连续区域、形态不定的场景	可以进行像素级缺陷分割, 但是依赖大量像素级标注数据, 标注成本很高
异常检测	通过自编码机、GAN、标准流等生成模型学习正常样本的表达方式, 根据重建误差、梯度或分布差异来进行缺陷检测	缺乏缺陷样本, 只有正常样本可以用于训练的场景	无需缺陷样本和标注, 可以检测未事先定义的缺陷类别, 但准确率尚达不到有监督学习的效果

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