AnomalyControl: Few-Shot Anomaly Generation by ControlNet Inpainting
Quality inspection tasks, i.e., anomaly detection, localization and classification, face the scarcity of non-nominal images in real industrial scenarios. Hence, generative models have been explored as a tool to obtain defective images from few real labelled samples. Despite the fast-increasing quali...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10806704/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Quality inspection tasks, i.e., anomaly detection, localization and classification, face the scarcity of non-nominal images in real industrial scenarios. Hence, generative models have been explored as a tool to obtain defective images from few real labelled samples. Despite the fast-increasing quality of such models, generating realistic defective images remains a challenging task due to the same data scarcity problem, which makes it difficult to steer large general-purpose models to produce realistic defects for specific industrial products. In this paper, we show how casting defect generation as inpainting of nominal images and using ControlNet to specialize a state-of-the-art inpainting model based on stable diffusion can be an effective solution for the few-shot anomaly generation task. Extensive experimental results on the MVTec-AD dataset demonstrate that the high quality of the images generated by our method significantly improves the state of the art on downstream anomaly classification. |
|---|---|
| ISSN: | 2169-3536 |