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              基于多尺度流模型的視覺異常檢測研究

              毛國君 吳星臻 邢樹禮

              毛國君, 吳星臻, 邢樹禮. 基于多尺度流模型的視覺異常檢測研究. 自動化學報, 2024, 50(3): 640?648 doi: 10.16383/j.aas.c230476
              引用本文: 毛國君, 吳星臻, 邢樹禮. 基于多尺度流模型的視覺異常檢測研究. 自動化學報, 2024, 50(3): 640?648 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): 640?648 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): 640?648 doi: 10.16383/j.aas.c230476

              基于多尺度流模型的視覺異常檢測研究

              doi: 10.16383/j.aas.c230476
              基金項目: 國家重點研發計劃(2019YFD0900905), 國家自然科學基金(61773415)資助
              詳細信息
                作者簡介:

                毛國君:福建理工大學計算機科學與數學學院教授. 主要研究方向為人工智能, 大數據, 數據挖掘和分布式計算. 本文通信作者. E-mail: 19662092@fjut.edu.cn

                吳星臻:福建理工大學計算機科學與數學學院碩士研究生. 主要研究方向為計算機視覺, 圖像處理和異常檢測. E-mail: xzwu@smail.fjut.edu.cn

                邢樹禮:福建理工大學計算機科學與數學學院講師. 主要研究方向為計算機視覺, 圖像處理和大數據分析. 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: 針對現有異常檢測(Anomaly detection, AD)模型計算效率低和檢測性能差等問題, 提出一種多尺度流模型(Multi-scale normalizing flow, MS-Flow), 通過多尺度交叉融合實現高效的視覺圖像異常識別. 具體地, 在流模型(Normalizing flow, NF)內部構建層級式的多尺度架構來避免多通道數據的冗余交叉計算, 同時保證網絡的多尺度表達能力. 此外, 設計的層級感知模塊通過逐層級的多粒度特征融合, 在細粒度級別表達多尺度特征, 有效地提高分布估計的精確性. 該方法是一個平衡檢測精度與計算效率的解決方案. 在兩個公開數據集上的實驗表明, 所提方法相較于以往的檢測模型能夠獲得更高的檢測精度(在MVTec AD和BTAD數據集上的平均AUROC (Area under the receiver operating characteristics)分別為99.7%和96.0%), 同時具有更高的計算效率, 浮點運算次數(Floating point operations, FLOPs)約為CS-Flow的1/8.
              • 圖  1  本文所提模型架構圖

                Fig.  1  The architecture of the proposed model

                圖  2  層級感知模塊結構圖

                Fig.  2  The structure of hierarchical perception module

                圖  3  MVTec AD和BTAD數據集中所有類別的樣例圖

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

                圖  4  不同流模型的測試圖像負對數似然分布

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

                圖  5  不同耦合層數的適應性實驗

                Fig.  5  Adaptation study of different coupling layers

                圖  6  異常定位

                Fig.  6  Anomaly localization

                表  1  MVTec AD和BTAD數據集的統計概述

                Table  1  Statistical overview of the MVTec AD and BTAD datasets

                類別訓練數據測試數據 (正常)測試數據 (異常)異常類型異常區域圖片尺寸(像素)
                MVTec AD (紋理)Carpet28028895971 024
                Grid264215751701 024
                Leather24532925991 024
                Tile2303384586840
                Wood247196051681 024
                MVTec AD (物體)Bottle2092063368900
                Cable224589281511 024
                Capsule2192310951141 000
                Hazelnut391407041361 024
                Metal Nut22022934132700
                Pill267261417245800
                Screw3204111951351 024
                Toothbrush6012301661 024
                Transistor21360404441 024
                Zipper2403211971771 024
                BTAD01400214911 600
                02399302001600
                031 000400411800
                總數量5 4289181 54876>1 888
                下載: 導出CSV

                表  2  不同異常檢測模型在MVTec AD數據集上的平均AUROC對比 (%)

                Table  2  The average AUROC of different anomaly detection models on MVTec AD dataset (%)

                類別DifferNet[33]CFlow-AD[34]CS-Flow[17]PatchCore[23]FastFlow[24]MS-Flow (本文)
                紋理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
                物體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
                下載: 導出CSV

                表  3  不同異常檢測模型在BTAD數據集上的平均AUROC對比 (%)

                Table  3  The average AUROC of different anomalydetection models on BTAD dataset (%)

                模型類別平均值
                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
                下載: 導出CSV

                表  4  不同流模型的復雜性對比

                Table  4  Complexity of different normalizing flows

                模型
                CFlow-ADCS-FlowFastFlowMS-Flow (本文)
                AUROC (%)98.398.799.499.7
                FLOPs (G)13.865.813.98.1
                Params (M)81.6275.217.714.1
                下載: 導出CSV

                表  5  不同特征提取器的適應性實驗

                Table  5  Adaptation study of different feature extractors

                特征提取網絡$d$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
                下載: 導出CSV

                表  6  不同子特征數的適應性實驗

                Table  6  Adaptation study of different subfeature numbers

                子特征數子特征圖尺寸(像素)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
                下載: 導出CSV
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