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Authors: Amr M. Nagy 1 ; 2 and László Czúni 2

Affiliations: 1 Faculty of Computers and Artificial Intelligence, Benha University, Egypt ; 2 Faculty of Information Technology, University of Pannonia, Egyetem u. 10, Veszprém, Hungary

Keyword(s): Visual Inspection, Defect Detection, Siamese Neural Network.

Abstract: Recently, the combination of deep learning algorithms with visual inspection technology allows differentiating anomalies in objects mimicking human visual inspection. While it offers precise and persistent monitoring with a minimum amount of human activity but to apply the same solution to a wide variety of defect types is challenging. In this paper, a new convolutional siamese neural model is presented to recognize different types of defects. One advantage of the proposed convolutional siamese neural network is that it can be used for new object types without re-training with much better performance than other siamese networks: it can generalize the knowledge of defect types and can apply it to new object classes. The proposed approach is tested with good results on two different data sets: one contains traffic signs of different types and different distortions, the other is a set of metal disk-shape castings with and without defects.

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Paper citation in several formats:
Nagy, A. and Czúni, L. (2021). Detecting Object Defects with Fusioning Convolutional Siamese Neural Networks. In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 5: VISAPP; ISBN 978-989-758-488-6; ISSN 2184-4321, SciTePress, pages 157-163. DOI: 10.5220/0010263301570163

@conference{visapp21,
author={Amr M. Nagy. and László Czúni.},
title={Detecting Object Defects with Fusioning Convolutional Siamese Neural Networks},
booktitle={Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 5: VISAPP},
year={2021},
pages={157-163},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010263301570163},
isbn={978-989-758-488-6},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 5: VISAPP
TI - Detecting Object Defects with Fusioning Convolutional Siamese Neural Networks
SN - 978-989-758-488-6
IS - 2184-4321
AU - Nagy, A.
AU - Czúni, L.
PY - 2021
SP - 157
EP - 163
DO - 10.5220/0010263301570163
PB - SciTePress

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