Unsupervised Anomaly Detection for Surface Defects with Dual-Siamese Network | |
Tao X(陶显)3; Da-Peng Zhang3; Ma WZ(马文治)3; Hou ZX(侯占新)2; Lu ZF(逯正峰)2; Chandranath Adak1 | |
刊名 | IEEE Transactions on Industrial Informatics |
2022-01 | |
卷号 | 1期号:1页码:1-11 |
关键词 | 缺陷检测 |
ISSN号 | 1551-3203 |
DOI | 10.1109/TII.2022.3142326 |
文献子类 | 长文 |
英文摘要 | Unsupervised anomaly detection in real industrial scenarios is challenging since the small amount of defect-free images contain limited discriminative information, and anomaly defects are unpredictable. In this paper, a dual-siamese network is designed to simultaneously detect and locate anomalies in images. It first uses a pre-trained convolutional neural network (CNN)-based siamese architecture to embed discriminative features of normal samples and synthetic defective samples. A dense feature fusion (DFF) module is employed to obtain the dense feature representation of dual input. The following siamese network of perceptual defects is proposed to reconstruct and restore the dual-dense features of the previous stage. Compared to the existing methods that only employ a single residual map, the restoration of dense feature maps is proposed to locate the anomalies better. The experimental results on the MVTec AD dataset demonstrate that our method achieves state-of-the-art inspection accuracy and has potential for industrial application. |
URL标识 | 查看原文 |
语种 | 英语 |
内容类型 | 期刊论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/47200] |
专题 | 精密感知与控制研究中心_精密感知与控制 |
通讯作者 | Tao X(陶显) |
作者单位 | 1.Indian Institute of Information Technology 2.China University of Mining and Technology - Beijing 3.Institute of Automation, Chinese Academy of Sciences |
推荐引用方式 GB/T 7714 | Tao X,Da-Peng Zhang,Ma WZ,et al. Unsupervised Anomaly Detection for Surface Defects with Dual-Siamese Network[J]. IEEE Transactions on Industrial Informatics,2022,1(1):1-11. |
APA | Tao X,Da-Peng Zhang,Ma WZ,Hou ZX,Lu ZF,&Chandranath Adak.(2022).Unsupervised Anomaly Detection for Surface Defects with Dual-Siamese Network.IEEE Transactions on Industrial Informatics,1(1),1-11. |
MLA | Tao X,et al."Unsupervised Anomaly Detection for Surface Defects with Dual-Siamese Network".IEEE Transactions on Industrial Informatics 1.1(2022):1-11. |
个性服务 |
查看访问统计 |
相关权益政策 |
暂无数据 |
收藏/分享 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论