The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
Improved anomaly detection by training an autoencoder with skip connections on images corrupted with Stain-shaped noise
Open accessAbstract
In industrial vision, the anomaly detection problem can be addressed with an autoencoder trained to map an arbitrary image, i.e. with or without any defect, to a clean image, i.e. without any defect. In this approach, anomaly detection relies conventionally on the reconstruction residual or, alternatively, on the reconstruction uncertainty. To improve the sharpness of the reconstruction, we consider an autoencoder architecture with skip connections. In the common scenario where only clean images are available for training, we propose to corrupt them with a synthetic noise model to prevent the convergence of the network towards the identity mapping, and introduce an original Stain noise model for that purpose. We show that this model favors the reconstruction of clean images from arbitrary real-world images, regardless of the actual defects appearance. In addition to demonstrating the relevance of our approach, our validation provides the first consistent assessment of reconstruction-based methods, by comparing their performance over the MVTec AD dataset [1], both for pixel- and image-wise anomaly detection. Our implementation is available at https://github.com/anncollin/AnomalyDetection-Keras.
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Cites
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BackgroundMVTec AD — A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection
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Semi-supervised deep learning for robust anomaly detection in smart manufacturing systems
Le, T. (2024) · theses.fr (ABES) · doi:10.70675/19d23228zf030z4df7za3abz596d36577818
EdgeDiff: Leveraging Edge Maps for Anomaly Detection with Diffusion Models
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Multi-Scale Feature Refinement Network for Lithography Hotspot Detection
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Metadata
- Authors
- Collin, A.-S., & De Vleeschouwer, C.
- Affiliations
- UCLouvain (BE)
- ORCID
- Collin, Anne-Sophie
- Venue
- 2020 25th International Conference on Pattern Recognition (ICPR)
- Publisher
- IEEE
- Published
- 2021-01-10
- Pages
- 7915-7922
- ISSN
- 1041-3278 (ISSN-L)
- OpenAlex
- W3082804066
- Cited by
- 106
- References
- 27
- Citations by year
- 2026: 8 · 2025: 22 · 2024: 33 · 2023: 17 · 2022: 18 · 2021: 8
- FWCI
- 26.09
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)) (submittedVersion, other-oa)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Keywords
- anomaly detection, autoencoder, skip connections, industrial vision, image reconstruction, reconstruction residuals, reconstruction uncertainty, MVTec AD
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