Learning Multi-Resolution Features for Unsupervised Anomaly Localization on Industrial Textured Surfaces
文献类型：期刊论文
| 作者 | Tao X(陶显) ; Shaohua Yan; Xinyi Gong; Chandranath Adak
|
| 刊名 | IEEE Transactions on Artificial Intelligence
 |
| 出版日期 | 2024-01 |
| 页码 | 1-13 |
| 英文摘要 | In industrial quality assessment, monitoring whether
the textured product contains defects is a critical step. Compared
to a large number of defect-free images that are easy to obtain,
anomaly samples are limited and vary randomly in size and type.
It is challenging to develop an automatic and accurate texture
defect localization system that only uses normal images for
training. In this paper, a multi-resolution feature learning
network is proposed to detect various texture defects in an
unsupervised manner. A robust pre-trained model is first
employed to extract the perceptual features from the input image,
then the perceptual features of various layers are fed to the
corresponding multi-scale autoencoder framework. This
hierarchical alignment strategy aids in receiving multi-level
information for locating anomalies of various sizes. Moreover, a
residual attention module (RAM) is embedded in the architecture
to further improve the detection performance. Our proposed
method has achieved state-of-the-art performance on the texture
dataset of MVTecAD. We also extended the experiment to the real
industrial texture datasets, and its detection result is better than
the major existing advanced techniques. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/57191]  |
| 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.e Department of Computer Science and Engineering, Indian Institute of Technology Patna 2.Institute of Automation, Chinese Academy of Sciences |
| 推荐引用方式 GB/T 7714 | Tao X,Shaohua Yan,Xinyi Gong,et al. Learning Multi-Resolution Features for Unsupervised Anomaly Localization on Industrial Textured Surfaces[J]. IEEE Transactions on Artificial Intelligence,2024:1-13. |
| APA | Tao X,Shaohua Yan,Xinyi Gong,&Chandranath Adak.(2024).Learning Multi-Resolution Features for Unsupervised Anomaly Localization on Industrial Textured Surfaces.IEEE Transactions on Artificial Intelligence,1-13. |
| MLA | Tao X,et al."Learning Multi-Resolution Features for Unsupervised Anomaly Localization on Industrial Textured Surfaces".IEEE Transactions on Artificial Intelligence (2024):1-13. |
入库方式： OAI收割
来源：自动化研究所
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