Authors:
João Soares
1
;
Luís Magalhães
1
;
Rafaela Pinho
2
;
Mehrab Allahdad
2
and
Manuel Ferreira
2
Affiliations:
1
ALGORITMI Research Centre / LASI, University of Minho, Portugal
;
2
Neadvance, Braga, Portugal
Keyword(s):
Machine Learning, Leather, Defects Detection, Novelty Detection.
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 resea
rch 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.
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