Abstract
Anomaly detectors are necessary to automatize industrial quality control. However, crafting such detectors is difficult due to the complexity and variability of the object even when working only with rigid objects. We show that adding a deep learning normalization step as a preprocessing step to model based detectors allows for better and more robust detections. This self-supervised normalization neural network is trained on non-anomalous data only. The proposed preprocessing method, followed by an automatic detector, achieves state-of-the-art results on rigid objects from the MvTec dataset.
| Original language | English |
|---|---|
| Title of host publication | 2021 IEEE International Conference on Image Processing, ICIP 2021, Proceedings |
| Publisher | IEEE |
| Pages | 989-993 |
| Number of pages | 5 |
| ISBN (Electronic) | 9781665441155 |
| ISBN (Print) | 9781665431026 |
| DOIs | |
| Publication status | Published - 2021 |
| Externally published | Yes |
| Event | 2021 IEEE International Conference on Image Processing - Anchorage, United States Duration: 19 Sept 2021 → 22 Sept 2021 |
Publication series
| Name | Proceedings - International Conference on Image Processing, ICIP |
|---|---|
| Volume | 2021-September |
| ISSN (Print) | 1522-4880 |
Conference
| Conference | 2021 IEEE International Conference on Image Processing |
|---|---|
| Country/Territory | United States |
| City | Anchorage |
| Period | 19/09/21 → 22/09/21 |
Bibliographical note
Publisher Copyright:© 2021 IEEE.
Funding
Work supported by a CIFRE scholarship of the French Ministry for Higher Studies, Research and Innovation.
Keywords
- Anomaly detection
- Deep-learning
- Denoising
- Self-similarity
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