Automatic Defect Detection in Leather

João Soares, Luís Magalhães, Rafaela Pinho, Mehrab Allahdad, Manuel Ferreira

2023

Abstract

Traditionally, leather defect detection is manually solved using specialized workers in the leather inspection process. However, this task is slow and prone to error. So, in the last two decades, distinct researchers proposed new solutions to automatize this procedure. At this moment, there are already efficient solutions in the literature review. However, these solutions are based on supervised machine learning techniques that require a high-dimension dataset. As the leather annotation process is time-consuming, it is necessary to find a solution to overcome this challenge. So, this research explores novelty detection techniques. Moreover, this work evaluates SSIM Autoencoder, CFLOW, STFPM, RDOCE, and DRAEM performances on leather defects detection problem. These techniques are trained and tested in two distinct datasets: MVTEC and Neadvance. These techniques present a good performance on MVTEC defects detection. However, they have difficulties with the Neadvance dataset. This research presents the best methodology to use for two distinct scenarios. When the real-world samples have only one color, DRAEM should be used. When the real-world samples have more than one color, the STFPM should be applied.

Paper Citation

in Harvard Style

Soares J., Magalhães L., Pinho R., Allahdad M. and Ferreira M. (2023). Automatic Defect Detection in Leather. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP; ISBN 978-989-758-634-7, SciTePress, pages 196-204. DOI: 10.5220/0011707000003417

in Bibtex Style

@conference{visapp23,
author={João Soares and Luís Magalhães and Rafaela Pinho and Mehrab Allahdad and Manuel Ferreira},
title={Automatic Defect Detection in Leather},
booktitle={Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP},
year={2023},
pages={196-204},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011707000003417},
isbn={978-989-758-634-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP
TI - Automatic Defect Detection in Leather
SN - 978-989-758-634-7
AU - Soares J.
AU - Magalhães L.
AU - Pinho R.
AU - Allahdad M.
AU - Ferreira M.
PY - 2023
SP - 196
EP - 204
DO - 10.5220/0011707000003417
PB - SciTePress