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基于多尺度流模型的视觉异常检测研究

毛国君 吴星臻 邢树礼

毛国君, 吴星臻, 邢树礼. 基于多尺度流模型的视觉异常检测研究. 自动化学报, 2024, 50(3): 1−9 doi: 10.16383/j.aas.c230476
引用本文: 毛国君, 吴星臻, 邢树礼. 基于多尺度流模型的视觉异常检测研究. 自动化学报, 2024, 50(3): 1−9 doi: 10.16383/j.aas.c230476
Mao Guo-Jun, Wu Xing-Zhen, Xing Shu-Li. Research on visual anomaly detection based on multi-scale normalizing flow. Acta Automatica Sinica, 2024, 50(3): 1−9 doi: 10.16383/j.aas.c230476
Citation: Mao Guo-Jun, Wu Xing-Zhen, Xing Shu-Li. Research on visual anomaly detection based on multi-scale normalizing flow. Acta Automatica Sinica, 2024, 50(3): 1−9 doi: 10.16383/j.aas.c230476

基于多尺度流模型的视觉异常检测研究

doi: 10.16383/j.aas.c230476
基金项目: 国家重点研发计划(2019YFD0900905), 国家自然科学基金(61773415)资助
详细信息
    作者简介:

    毛国君:福建理工大学计算机科学与数学学院教授. 主要研究方向为人工智能, 大数据, 数据挖掘和分布式计算. 本文通信作者. E-mail: 19662092@fjut.edu.cn

    吴星臻:福建理工大学计算机科学与数学学院硕士研究生. 主要研究方向为计算机视觉, 图像处理和异常检测. E-mail: xzwu@smail.fjut.edu.cn

    邢树礼:福建理工大学计算机科学与数学学院讲师. 主要研究方向为计算机视觉, 图像处理和大数据分析. E-mail: 19892311@fjut.edu.cn

Research on Visual Anomaly Detection Based on Multi-scale Normalizing Flow

Funds: Supported by National Key Research and Development Program of China (2019YFD0900905) and National Natural Science Foundation of China (61773415)
More Information
    Author Bio:

    MAO Guo-Jun Professor at the College of Computer Science and Mathematics, Fujian University of Technology. His research interest covers artificial intelligence, big data, data mining, and distributed computing. Corresponding author of this paper

    WU Xing-Zhen Master student at the College of Computer Science and Mathematics, Fujian University of Technology. His research interest covers computer vision, image processing, and anomaly detection

    XING Shu-Li Lecturer at the College of Computer Science and Mathematics, Fujian University of Technology. His research interest covers computer vision, image processing, and big data analytics

  • 摘要: 针对现有异常检测(Anomaly detection, AD)模型计算效率低和检测性能差等问题, 提出一种多尺度流模型(Normalizing flow, NF), 通过多尺度交叉融合实现高效的视觉图像异常识别. 具体地, 在流模型内部构建层级式的多尺度架构来避免多通道数据的冗余交叉计算, 同时保证网络的多尺度表达能力. 此外, 设计的层级感知模块通过逐层级的多粒度特征融合, 在细粒度级别表达多尺度特征, 有效地提高分布估计的精确性. 该方法是一个平衡检测精度与计算效率的解决方案. 在两个公开数据集上的实验表明, 所提方法相较于以往的检测模型能够获得更高的检测精度(在MVTec AD和BTAD数据集上的平均AUROC (Area under the receiver operating characteristics)分别为99.7%和96.0%), 同时具有更高的计算效率, 浮点运算次数(Floating point operations, FLOPs)约为CS-Flow的1/8.
  • 图  1  本文所提模型架构图

    Fig.  1  The architecture of the proposed model

    图  2  层级感知模块结构图

    Fig.  2  The structure of hierarchical perception module

    图  3  MVTec AD和BTAD数据集中所有类别的样例图

    Fig.  3  Example images for all categories of the MVTec AD and BTAD datasets

    图  4  不同流模型的测试图像负对数似然分布

    Fig.  4  Negative log-likelihood distribution of test images for different normalizing flow

    图  5  不同耦合层数的适应性实验

    Fig.  5  Adaptation study of different coupling layers

    图  6  异常定位

    Fig.  6  Anomaly localization

    表  1  MVTec AD和BTAD数据集的统计概述

    Table  1  Statistical overview of the MVTec AD and BTAD datasets

    类别训练数据测试数据 (正常)测试数据 (异常)异常类型异常区域图片尺寸
    MVTec AD (纹理)Carpet28028895971 024
    Grid264215751701 024
    Leather24532925991 024
    Tile2303384586840
    Wood247196051681 024
    MVTec AD (物体)Bottle2092063368900
    Cable224589281511 024
    Capsule2192310951141 000
    Hazelnut391407041361 024
    Metal Nut22022934132700
    Pill267261417245800
    Screw3204111951351 024
    Toothbrush6012301661 024
    Transistor21360404441 024
    Zipper2403211971771 024
    BTAD01400214911 600
    02399302001600
    031 000400411800
    总数量5 4289181 548761 888 $ \uparrow$
    下载: 导出CSV

    表  2  不同异常检测模型在MVTec AD数据集上的性能对比AUROC (%)

    Table  2  Performance of different anomaly detection models on MVTec AD dataset (%)

    类别DifferNet[33]CFlow-AD[34]CS-Flow[17]PatchCore[23]FastFlow[24]MS-Flow (本文)
    纹理Carpet92.998.7100.098.7100.0100.0
    Grid84.099.699.098.299.7100.0
    Leather97.1100.0100.0100.0100.0100.0
    Tile99.499.8100.098.7100.0100.0
    Wood99.899.1100.099.2100.0100.0
    物体Bottle99.0100.099.8100.0100.0100.0
    Cable95.997.699.199.5100.099.6
    Capsule86.997.797.198.1100.099.4
    Hazelnut99.399.999.6100.0100.0100.0
    Metal Nut96.199.399.1100.0100.0100.0
    Pill88.896.898.696.699.499.5
    Screw96.391.997.698.197.897.5
    Toothbrush98.699.791.9100.094.4100.0
    Transistor91.195.299.3100.099.8100.0
    Zipper95.198.599.799.499.599.8
    平均值94.998.398.799.199.499.7
    下载: 导出CSV

    表  3  不同异常检测模型在BTAD数据集上的性能对比AUROC (%)

    Table  3  Performance of different anomaly detection models on BTAD dataset (%)

    模型类别平均值
    010203
    VT-ADL[36]97.671.082.683.7
    SPADE[22]91.471.499.987.6
    PatchCore[23]90.979.399.890.0
    PaDiM[28]99.882.099.493.7
    MS-Flow (本文)99.988.2100.096.0
    下载: 导出CSV

    表  4  不同流模型的复杂性对比

    Table  4  Complexity of different normalizing flows

    模型CFlowCS-FlowFastFlowMS-Flow (本文)
    AUROC (%)98.398.799.499.7
    FLOPs (G)13.865.813.98.1
    Params (M)81.6275.217.714.1
    下载: 导出CSV

    表  5  不同特征提取器的适应性实验

    Table  5  Adaptation study of different feature extractors

    特征提取网络$d$AUROC (%)
    ResNet1897.1 $\rightarrow$ 97.9 $\rightarrow$ 97.2
    Wide-ResNet5097.9 $\rightarrow$ 96.2 $\rightarrow$ 93.6
    Swin-B 224 $\rightarrow$ 448 $\rightarrow$ 76896.9 $\rightarrow$ 97.8 $\rightarrow$ 95.4
    EfficientNet-B798.7 $\rightarrow$ 99.1 $\rightarrow$ 99.5
    EfficientNet-B598.8 $\rightarrow$ 99.3 $\rightarrow$ 99.7
    下载: 导出CSV

    表  6  不同子特征数的适应性实验

    Table  6  Adaptation study of different subfeature numbers

    子特征数子特征图尺寸AUROC (%)Params (M)
    2$152 \times 24 \times 24$96.219.42
    4$76 \times 24 \times 24$99.7214.06
    6$51 \times 24 \times 24$99.7915.74
    8$38 \times 24 \times 24$99.7916.43
    下载: 导出CSV
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  • 收稿日期:  2023-08-02
  • 录用日期:  2023-08-31
  • 网络出版日期:  2024-01-04

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