CSE: Surface Anomaly Detection with Contrastively Selected Embedding

Simon Thomine, Simon Thomine, Hichem Snoussi

2024

Abstract

Detecting surface anomalies of industrial materials poses a significant challenge within a myriad of industrial manufacturing processes. In recent times, various methodologies have emerged, capitalizing on the advantages of employing a network pre-trained on natural images for the extraction of representative features. Subsequently, these features are subjected to processing through a diverse range of techniques including memory banks, normalizing flow, and knowledge distillation, which have exhibited exceptional accuracy. This paper revisits approaches based on pre-trained features by introducing a novel method centered on target-specific embedding. To capture the most representative features of the texture under consideration, we employ a variant of a contrastive training procedure that incorporates both artificially generated defective samples and anomaly-free samples during training. Exploiting the intrinsic properties of surfaces, we derived a meaningful representation from the defect-free samples during training, facilitating a straightforward yet effective calculation of anomaly scores. The experiments conducted on the MVTEC AD and TILDA datasets demonstrate the competitiveness of our approach compared to state-of-the-art methods.

Paper Citation

in Harvard Style

Thomine S. and Snoussi H. (2024). CSE: Surface Anomaly Detection with Contrastively Selected Embedding. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP; ISBN 978-989-758-679-8, SciTePress, pages 280-289. DOI: 10.5220/0012546700003660

in Bibtex Style

@conference{visapp24,
author={Simon Thomine and Hichem Snoussi},
title={CSE: Surface Anomaly Detection with Contrastively Selected Embedding},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP},
year={2024},
pages={280-289},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012546700003660},
isbn={978-989-758-679-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP
TI - CSE: Surface Anomaly Detection with Contrastively Selected Embedding
SN - 978-989-758-679-8
AU - Thomine S.
AU - Snoussi H.
PY - 2024
SP - 280
EP - 289
DO - 10.5220/0012546700003660
PB - SciTePress