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图像异常检测研究现状综述

吕承侃 沈飞 张正涛 张峰

吕承侃, 沈飞, 张正涛, 张峰. 图像异常检测研究现状综述. 自动化学报, 2022, 48(6): 1402−1428 doi: 10.16383/j.aas.c200956
引用本文: 吕承侃, 沈飞, 张正涛, 张峰. 图像异常检测研究现状综述. 自动化学报, 2022, 48(6): 1402−1428 doi: 10.16383/j.aas.c200956
Lv Cheng-Kan, Shen Fei, Zhang Zheng-Tao, Zhang Feng. Review of image anomaly detection. Acta Automatica Sinica, 2022, 48(6): 1402−1428 doi: 10.16383/j.aas.c200956
Citation: Lv Cheng-Kan, Shen Fei, Zhang Zheng-Tao, Zhang Feng. Review of image anomaly detection. Acta Automatica Sinica, 2022, 48(6): 1402−1428 doi: 10.16383/j.aas.c200956

图像异常检测研究现状综述

doi: 10.16383/j.aas.c200956
基金项目: 中国科学院青年创新促进会(2020139)资助
详细信息
    作者简介:

    吕承侃:中国科学院自动化研究所精密感知与控制研究中心博士研究生. 2017年获得山东大学学士学位. 主要研究方向为神经网络, 计算机视觉与图像处理. E-mail: lvchengkan2017@ia.ac.cn

    沈飞:中国科学院自动化研究所精密感知与控制研究中心研究员. 2012年获得中国科学院自动化研究所博士学位. 主要研究方向为视觉检测, 机器人视觉控制与微装配. E-mail: fei.shen@ia.ac.cn

    张正涛:中国科学院自动化研究所精密感知与控制研究中心研究员. 2010年获得中国科学院自动化研究所博士学位. 主要研究方向为视觉测量, 微装配与自动化. 本文通信作者. E-mail: zhengtao.zhang@ia.ac.cn

    张峰:中国科学院自动化研究所精密感知与控制研究中心副研究员. 2012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人控制, 机器人视觉控制与微装配. E-mail: feng.zhang@ia.ac.cn

Review of Image Anomaly Detection

Funds: Supported by Youth Innovation Promotion Association, Chinese Academy of Sciences (2020139)
More Information
    Author Bio:

    LV Cheng-Kan Ph. D. candidate at the Research Center of Precision Sensing and Control, Institute of Automation, Chinese Academy of Sciences. He received his bachelor degree from Shandong University in 2017. His research interest covers neural networks, computer vision and image processing

    SHEN Fei Professor at the Research Center of Precision Sensing and Control, Institute of Automation, Chinese Academy of Sciences. He received his Ph. D. degree from Institute of Automation, Chinese Academy of Sciences in 2012. His research interest covers visual inspection, robot vision control and micro-assembly

    ZHANG Zheng-Tao Professor at the Research Center of Precision Sensing and Control, Institute of Automation, Chinese Academy of Sciences. He received his Ph. D. degree from Institute of Automation, Chinese Academy of Sciences in 2010. His research interest covers visual measurement, micro-assembly and automation. Corresponding author of this paper

    ZHANG Feng Associate professor at the Research Center of Precision Sensing and Control, Institute of Automation, Chinese Academy of Sciences. He received his Ph. D. degree from Institute of Automation, Chinese Academy of Sciences in 2012. His research interest covers robot control, robot vision control and micro-assembly

  • 摘要: 图像异常检测是计算机视觉领域的一个热门研究课题, 其目标是在不使用真实异常样本的情况下, 利用现有的正常样本构建模型以检测可能出现的各种异常图像, 在工业外观缺陷检测、医学图像分析、高光谱图像处理等领域有较高的研究意义和应用价值. 本文首先介绍了异常的定义以及常见的异常类型. 然后, 本文根据在模型构建过程中有无神经网络的参与, 将图像异常检测方法分为基于传统方法和基于深度学习两大类型, 并分别对相应的检测方法的设计思路、优点和局限性进行了综述与分析. 其次, 梳理了图像异常检测任务中面临的主要挑战. 最后, 对该领域未来可能的研究方向进行了展望.
  • 图  1  异常的类型[1]

    Fig.  1  The type of anomaly[1]

    图  2  图像异常分类图

    Fig.  2  The classification of image anomalies

    图  3  图像异常检测技术的分类图

    Fig.  3  The classification of image anomaly detection methods

    图  4  模板匹配的适用场景[48]

    Fig.  4  Applicable scenes of template matching[48]

    图  5  统计模型的适用场景[27, 51-52]

    Fig.  5  Applicable scenes of statistical model[27, 51-52]

    图  6  基于低秩分解的图像异常检测示意图[57]

    Fig.  6  Illustration of anomaly detection based on low-rank decomposition[57]

    图  7  图像分解的适用场景

    Fig.  7  Applicable scenes of image decomposition

    图  8  通过背景频谱消除来进行异常检测[66]

    Fig.  8  Anomaly detection based on subtraction of background spectral[66]

    图  9  人工构造周期性示意图

    Fig.  9  Illustration of artificial periodicity

    图  10  周期性较弱时的检测效果[62]

    Fig.  10  Detection result in weakly periodic scene[62]

    图  11  稀疏编码中的字典[71]

    Fig.  11  The dictionary learned in sparse encoding[71]

    图  12  纳米材料图像[71]

    Fig.  12  Image of nanofibres[71]

    图  13  OC-SVM和SVDD的示意图[82]

    Fig.  13  Illustration of OC-SVM and SVDD[82]

    图  14  Deep SVDD的原理示意图[88]

    Fig.  14  Illustration of Deep SVDD[88]

    图  15  FCDD的原理示意图[96]

    Fig.  15  Illustration of FCDD[96]

    图  16  在旋转目标上的检测效果[96]

    Fig.  16  Detection results on targets with rotation[96]

    图  17  将单类样本转换成多类样本[101]

    Fig.  17  Transforming one-class samples into multi-class samples[101]

    图  18  不同图像上旋转效果对比[102]

    Fig.  18  Comparison of rotation on different images[102]

    图  19  GAN结构示意图[15]

    Fig.  19  The structure of GAN[15]

    图  20  需要构建负样本的几种方法的示意图

    Fig.  20  The graphical illustration of the methods based on creating fake-negative samples

    图  21  自编码器的结构[119]

    Fig.  21  The structure of autoencoder[119]

    图  22  异常样本的重构示意图[130]

    Fig.  22  The reconstruction of anomalous images[130]

    图  23  隐变量编辑示意图[132]

    Fig.  23  The editing of latent vector[132]

    图  24  结合自编码器和GAN进行图像重构[107]

    Fig.  24  Image reconstruction based on autoencoder and GAN[107]

    图  25  医学图像的重构[16, 142]

    Fig.  25  Reconstruction of medical images[16, 142]

    图  26  工业图像中的微小异常[146]

    Fig.  26  Tiny anomaly in industrial image[146]

    表  1  图像异常检测的应用领域

    Table  1  Applications of image anomaly detection

    应用领域具体应用
    缺陷检测各种产品表面缺陷检测, 包括布匹[8]、玻璃[9]、钢板[10]、水泥[11]等纹理表面以及印制电路板[12]、酒瓶[13]等物体表面缺陷的检测
    医学影像分析在核磁共振图像[14]、虹膜图像[15]、眼底视网膜图像[16]等医学图像中检测可能的病变区域
    高光谱图像处理海面船舶检测[17]、地面异常区域检测[18]、机场飞机定位[19]
    下载: 导出CSV

    表  2  基于传统方法的图像异常检测技术的分类和特点

    Table  2  The classification and characteristic of traditional image anomaly detection methods

    方法类别设计思路优点缺点参考文献
    模板匹配建立待测图像和模板图像之间的对应关系, 通过比较得到异常区域方法简单有效, 对于采集环境高度可控的场景有很高的检测精度不适用于多变的场景或目标[2948]
    统计模型通过统计学方法构建背景模型具有详实的理论基础和推导过程, 检测速度快需要大量的训练样本, 且仅适用于一些简单背景下的异常检测[4954]
    图像分解将原始图像分解成代表背景的低秩矩阵和代表异常区域的稀疏矩阵具有详实的理论基础且无需训练过程速度较慢, 而且不适合在结构复杂的图像中进行异常检测[5661]
    频域分析通过编辑图像的频谱信息来消除图像中重复的背景纹理部分以凸显异常区域无需训练过程, 检测速度很快还需更详实的理论论证, 且仅适用于一些有重复性纹理的图像, 通用性较差[6470]
    稀疏编码重构借助稀疏编码和字典学习等方式学习正常样本的表示方法, 从重构误差和稀疏度等角度检测异常适用于各种类型的图像, 通用性很好检测时间长, 而且需要额外的空间保存过完备的字典.[7178]
    分类面构建建立分类面将现有的正常样本和潜在的异常样本进行区分通用性较好, 且速度较快各项参数的选择过程较为复杂[8187]
    下载: 导出CSV

    表  3  基于深度学习的图像异常检测技术的分类和特点

    Table  3  The classification and characteristic of deep learning based image anomaly detection

    方法类别设计思路优点缺点参考文献
    距离度量将正常图像映射到指定区域内, 并减小正常特征之间距离, 根据待测图像的特征到聚类中心的距离进行异常检测模型结构简单, 适用范围广模型可能出现退化, 需要设计额外的辅助任务, 且无法准确定位异常区域[8898]
    分类面构建通过几何变换增广现有数据, 直接训练分类模型并利用置信度来检测异常模型训练较为简单, 语义信息提取能力更强, 异常检测精度很高几何变换的操作在纹理图像等场景下并不适用[101102]
    寻找与正常样本近似的图像作为负样本来训练二分类网络, 构建正常图像与潜在异常图像间的分类面应用场景广泛, 异常检测精度高需要精心设计损失函数和生成的负样本, 模型设计复杂[104117]
    图像重构利用自编码器等模型学习正常图像的表达方式, 并根据待测图像的重构误差来进行异常检测训练阶段无需引入额外的样本, 且应用场景广泛, 速度较快一般的方法重构结果较为模糊, 且缺乏更为高效可靠的方法避免重构出异常区域[118134]
    利用GAN来获得更为清晰的图像重构效果应用场景广泛, 异常区域定位精度高模型训练复杂, 而且缺乏理论上的保证[135147]
    结合传统方法利用预训练的网络或者自编码器模型对图像进行特征提取, 在决策阶段利用传统方法进行异常检测相比传统方法精度更高通用性更好, 且速度较快在检测精度上略有不足[150160]
    下载: 导出CSV

    表  4  图像异常检测常用数据集

    Table  4  Common datasets for image anomaly detection

    应用场景数据集名称参考文献
    工业布匹TILDA[161]
    PFID[162]
    金属MT[163]
    RSDD[164]
    NEU[165]
    纳米材料NanoTWICE[71]
    综合MVTec AD[146]
    医学大脑BraTS[167]
    视网膜AMD[168]
    高光谱混合AVIRIS[169]
    ABU[170]
    下载: 导出CSV

    表  5  各图像异常定位方法在MVTec AD上的性能

    Table  5  Performance of image anomaly localization methods on MVTec AD

    方法大致思路定位性能
    AUROCPRO-score
    AE[146]利用自编码器进行图像重构0.8170.790
    AnoGAN[15]利用GAN中的生成器进行图像重构0.7430.443
    Iterative Projection[134]在图像重构基础上采用迭代优化寻找最优的正常图像0.893
    AESc[172]利用蒙特卡洛对重构网络进行Dropout并利用预测不确定性进行异常定位0.86
    P-Net[16]在图像重构过程中添加对纹理结构的约束0.89
    Uninformed Students[97]联合考虑待测图特征到目标特征之间的距离和方差进行异常定位0.857
    CAVGA[144]在图像重构的基础上采用注意力图定位异常区域0.93
    FCDD[96]利用全卷积网络提取特征并以偏置项作为特征映射中心0.96
    Patch SVDD[173]计算待检图像片和最近似的正常图像片之间的距离进行异常定位0.957
    PaDiM[171]用预训练的网络进行特征提取, 利用多维高斯模型进行异常定位0.9750.921
    SPADE[174]寻找待测样本的K-近邻正常图像作为参考, 再通过距离度量进行异常检测0.9650.917
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-11-18
  • 录用日期:  2021-06-25
  • 网络出版日期:  2021-09-06
  • 刊出日期:  2022-06-02

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