Analysis of the Future Potential of Autoencoders in Industrial Defect Detection

Sarah Schneider, Sarah Schneider, Doris Antensteiner, Daniel Soukup, Matthias Scheutz

2022

Abstract

We investigated the anomaly detection behaviour of three convolutional autoencoder types - a “standard” convolutional autoencoder (CAE), a variational convolutional autoencoder (VAE) and an adversarial convolutional autoencoder (AAE) - by applying them to different visual anomaly detection scenarios. First, we utilized our three autoencoder types to detect anomalous regions in two synthetically generated datasets. To investigate the convolutional autoencoders’ defect detection performances “in the industrial wild”, we applied the models on quality inspection images of non-defective and defective material regions. We compared the performances of all three autoencoder types based on their ability to detect anomalies and captured the training complexity by measuring the time needed for training them. Although the CAE is the simplest model, the trained model performed nearly as well as the more sophisticated autoencoder types, which depend on more complex training processes. For data that lacks regularity or shows purely stochastic patterns, all our autoencoders failed to compute meaningful results.

Paper Citation

in Harvard Style

Schneider S., Antensteiner D., Soukup D. and Scheutz M. (2022). Analysis of the Future Potential of Autoencoders in Industrial Defect Detection. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP; ISBN 978-989-758-555-5, SciTePress, pages 282-289. DOI: 10.5220/0010777000003124

in Bibtex Style

@conference{visapp22,
author={Sarah Schneider and Doris Antensteiner and Daniel Soukup and Matthias Scheutz},
title={Analysis of the Future Potential of Autoencoders in Industrial Defect Detection},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP},
year={2022},
pages={282-289},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010777000003124},
isbn={978-989-758-555-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP
TI - Analysis of the Future Potential of Autoencoders in Industrial Defect Detection
SN - 978-989-758-555-5
AU - Schneider S.
AU - Antensteiner D.
AU - Soukup D.
AU - Scheutz M.
PY - 2022
SP - 282
EP - 289
DO - 10.5220/0010777000003124
PB - SciTePress