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Authors: Richárd Rádli and László Czúni

Affiliation: Faculty of Information Technology, University of Pannonia, Egyetem Street 10., Veszprém, Hungary

Keyword(s): Autoencoder Neural Network, Convolutional Neural Network, Defect Detection, Unsupervised Anomaly Detection, Spatial Transformer Network.

Abstract: Autoencoders (AE) can have an important role in visual inspection since they are capable of unsupervised learning of normal visual appearance and detection of visual defects as anomalies. Reducing the variability of incoming structures can result in more efficient representation in latent space and better reconstruction quality for defect free inputs. In our paper we investigate the utilization of spatial transformer networks (STN) to improve the efficiency of AEs in reconstruction and defect detection. We found that the simultaneous training of the convolutional layers of the AEs and the weights of STNs doesn’t result in satisfactory reconstructions by the decoder. Instead, the STN can be trained to normalize the orientation of the input images. We evaluate the performance of the proposed mechanism, on three classes of input patterns, by reconstruction error and standard anomaly detection metrics.

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Paper citation in several formats:
Rádli, R. and Czúni, L. (2022). Improving the Efficiency of Autoencoders for Visual Defect Detection with Orientation Normalization. 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; ISSN 2184-4321, SciTePress, pages 651-658. DOI: 10.5220/0010903600003124

@conference{visapp22,
author={Richárd Rádli. and László Czúni.},
title={Improving the Efficiency of Autoencoders for Visual Defect Detection with Orientation Normalization},
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={651-658},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010903600003124},
isbn={978-989-758-555-5},
issn={2184-4321},
}

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 - Improving the Efficiency of Autoencoders for Visual Defect Detection with Orientation Normalization
SN - 978-989-758-555-5
IS - 2184-4321
AU - Rádli, R.
AU - Czúni, L.
PY - 2022
SP - 651
EP - 658
DO - 10.5220/0010903600003124
PB - SciTePress

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