基于异常特征对抗学习的工业图像异常检测方法
王天飞,周文俊,项圣,贺宇航,彭博

Industrial image anomaly detection method based on adversarial learning of abnormal features
Tianfei WANG,Wenjun ZHOU,Sheng XIANG,Yuhang HE,Bo PENG
表 2 不同方法图像级/像素级AUROC结果对比
Tab.2 Image- and pixel-level AUROC comparison results for different models
类别AUROC
USAE-SSIMRIADPaDimCutPasteCLGANMB-PFMATSNM本研究算法
1)注:斜线前、后数据分别表示图像级以及像素级AUROC结果
bottle99.0/97.81)88.0/93.099.9/98.499.9/98.398.2/97.697.6/92.6100.0/98.4100.0/98.396.8/98.5
capsule86.1/96.861.0/94.088.4/92.891.3/98.598.2/97.498.2/98.494.5/94.393.7/98.596.2/97.3
grid81.0/89.969.0/94.099.6/98.896.7/97.3100.0/97.599.3/98.798.0/98.895.2/98.7100.0/99.6
leather88.2/97.846.0/78.0100.0/99.4100.0/99.2100.0/99.5100.0/99.7100.0/96.4100.0/99.5100.0/99.9
pill87.9/96.560.0/91.083.8/95.793.3/95.794.9/95.798.1/97.396.5/95.293.7/96.598.2/96.5
tile99.1/92.552.0/59..098.7/89.198.1/98.194.6/90.596.5/94.199.6/96.295.9/97.9100.0/98.6
transistor81.8/97.852.0/90.090.9/87.797.4/97.596.1/93.096.4/93.397.8/97.891.6/87.595.0/92.1
zipper91.9/95.680.0/88.098.1/97.890.3/98.599.9/99.399.3/97.897.4/98.296.3/98.5100.0/99.1
cable86.2/91.961.0/82.081.9/84.292.7/96.781.2/90.098.3/95.698.8/96.791.3/96.893.2/96.7
carpet91.6/93.567.0/87.084.2/96.399.8/99.193.9/98.398.2/97.8100.0/99.297.8/98.396.5/99.2
hazelnut93.1/98.254.0/97.083.3/96.192.0/98.298.3/97.399.0/98.1100.0/99.199.8/98.4100.0/98.8
metalnut82.0/97.254.0/89.088.5/92.598.7/97.299.9/93.197.9/96.8100.0/97.298.6/96.798.9/97.8
screw54.9/97.451.0/92.084.5/98.885.8/98.588.7/96.795.2/94.991.8/97.792.1/98.996.6/98.9
toothbrush95.3/97.974.0/96.0100.0/98.996.1/98.899.4/98.198.2/96.688.6/98.691.4/98.9100.0/97.9
wood97.7/92.183.0/73.093.0/85.899.2/94.999.1/95.598.9/96.999.5/95.698.8/96.997.6/94.5
平均89.7/95.363.4/88.091.3/94.295.3/97.596.1/96.097.5/95.197.5/97.395.7/97.498.2/97.8