Deep Industrial Image Anomaly Detection: A Survey
Fuente:
arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866917626267041792 |
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| author | Liu, Jiaqi Xie, Guoyang Wang, Jinbao Li, Shangnian Wang, Chengjie Zheng, Feng Jin, Yaochu |
| author_facet | Liu, Jiaqi Xie, Guoyang Wang, Jinbao Li, Shangnian Wang, Chengjie Zheng, Feng Jin, Yaochu |
| contents | The recent rapid development of deep learning has laid a milestone in industrial Image Anomaly Detection (IAD). In this paper, we provide a comprehensive review of deep learning-based image anomaly detection techniques, from the perspectives of neural network architectures, levels of supervision, loss functions, metrics and datasets. In addition, we extract the new setting from industrial manufacturing and review the current IAD approaches under our proposed our new setting. Moreover, we highlight several opening challenges for image anomaly detection. The merits and downsides of representative network architectures under varying supervision are discussed. Finally, we summarize the research findings and point out future research directions. More resources are available at https://github.com/M-3LAB/awesome-industrial-anomaly-detection. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2301_11514 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Deep Industrial Image Anomaly Detection: A Survey Liu, Jiaqi Xie, Guoyang Wang, Jinbao Li, Shangnian Wang, Chengjie Zheng, Feng Jin, Yaochu Computer Vision and Pattern Recognition The recent rapid development of deep learning has laid a milestone in industrial Image Anomaly Detection (IAD). In this paper, we provide a comprehensive review of deep learning-based image anomaly detection techniques, from the perspectives of neural network architectures, levels of supervision, loss functions, metrics and datasets. In addition, we extract the new setting from industrial manufacturing and review the current IAD approaches under our proposed our new setting. Moreover, we highlight several opening challenges for image anomaly detection. The merits and downsides of representative network architectures under varying supervision are discussed. Finally, we summarize the research findings and point out future research directions. More resources are available at https://github.com/M-3LAB/awesome-industrial-anomaly-detection. |
| title | Deep Industrial Image Anomaly Detection: A Survey |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2301.11514 |