arXiv Open Access 2023

Deep Industrial Image Anomaly Detection: A Survey

Jiaqi Liu Guoyang Xie Jinbao Wang Shangnian Li Chengjie Wang +2 lainnya

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Abstrak

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.

Topik & Kata Kunci

cs.CV

Penulis (7)

Jiaqi Liu

Guoyang Xie

Jinbao Wang

Shangnian Li

Chengjie Wang

Feng Zheng

Yaochu Jin

Format Sitasi

APA MLA BibTeX

Liu, J., Xie, G., Wang, J., Li, S., Wang, C., Zheng, F. et al. (2023). Deep Industrial Image Anomaly Detection: A Survey. https://arxiv.org/abs/2301.11514

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Informasi Jurnal

Tahun Terbit: 2023
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓