<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In recent years, there has been a highly competitive pressure on industrial production. To keep ahead of the competition, emerging technologies must be developed and incorporated. Automated visual inspection systems, which improve the overall mass production quantity and quality in lines, are crucial. The modifications of the inspection system involve excessive time and money costs. Therefore, these systems should be flexible in terms of fulfilling the changing requirements of high capacity production support. A coherent defect detection model as a primary application to be used in a real-time intelligent visual surface inspection system is proposed in this paper. The method utilizes a new approach consisting of nested autoencoders trained with defect-free and defect injected samples to detect defects. Making use of two nested autoencoders, the proposed approach shows great performance in eliminating defects. The first autoencoder is used essentially for feature extraction and reconstructing the image from these features. The second one is employed to identify and fix defects in the feature code. Defects are detected by thresholding the difference between decoded feature code outputs of the first and the second autoencoder. The proposed model has a 96% detection rate and a relatively good segmentation performance while being able to inspect fabrics driven at high speeds.</p>
</abstract>

In recent years, there has been a highly competitive pressure on industrial production. To keep ahead of the competition, emerging technologies must be developed and incorporated. Automated visual inspection systems, which improve the overall mass production quantity and quality in lines, are crucial. The modifications of the inspection system involve excessive time and money costs. Therefore, these systems should be flexible in terms of fulfilling the changing requirements of high capacity production support. A coherent defect detection model as a primary application to be used in a real-time intelligent visual surface inspection system is proposed in this paper. The method utilizes a new approach consisting of nested autoencoders trained with defect-free and defect injected samples to detect defects. Making use of two nested autoencoders, the proposed approach shows great performance in eliminating defects. The first autoencoder is used essentially for feature extraction and reconstructing the image from these features. The second one is employed to identify and fix defects in the feature code. Defects are detected by thresholding the difference between decoded feature code outputs of the first and the second autoencoder. The proposed model has a 96% detection rate and a relatively good segmentation performance while being able to inspect fabrics driven at high speeds.

A Nested Autoencoder Approach to Automated Defect Inspection on Textured Surfaces

Department of Control and Automation Engineering

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

In recent years, there has been a highly competitive pressure on industrial production. To keep ahead of the competition, emerging technologies must be...

A Nested Autoencoder Approach to Automated Defect Inspection on Textured Surfaces

Data publikacji: 27 wrz 2021

Zakres stron: 515 - 523

Otrzymano: 06 mar 2021

Przyjęty: 10 cze 2021

DOI: https://doi.org/10.34768/amcs-2021-0035

Słowa kluczowe
autoencoders, defect detection, automatic visual inspection, deep learning

© 2021 Muhammed Ali Nur Oz et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

A Nested Autoencoder Approach to Automated Defect Inspection on Textured Surfaces

Data publikacji: 27 wrz 2021

Zakres stron: 515 - 523

Otrzymano: 06 mar 2021

Przyjęty: 10 cze 2021

DOI: https://doi.org/10.34768/amcs-2021-0035

Słowa kluczoweautoencoders, defect detection, automatic visual inspection, deep learning

© 2021 Muhammed Ali Nur Oz et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Słowa kluczowe
autoencoders, defect detection, automatic visual inspection, deep learning