Thesis Details
Automatická analýza obrazu pro kontrolu kvality výroby textilií
This work deals with the classification of defects that occur in the production of nonwovens. The defect classification task is part of a system for automatic production quality control. The goal is to implement a method that will classify problematic defect classes with sufficient accuracy. That was achieved using convolutional neural networks (CNN). The best results were achieved by the EfficientNet network, which had an accuracy of 81% when evaluated by cross-validation on an available dataset. Within the work, a number of experiments are performed, which are focused on the modification of input data. The influence of the shape and composition of the input images on the final classification is examined. A CNN model was also implemented, which uses additional information for classification in addition to the image.
defect classification, nonwoven fabric, quality control, convolutional neural network, image processing
Beran Vítězslav, doc. Ing., Ph.D. (DCGM FIT BUT), člen
Čadík Martin, doc. Ing., Ph.D. (DCGM FIT BUT), člen
Juránek Roman, Ing., Ph.D. (DCGM FIT BUT), člen
Křivka Zbyněk, Ing., Ph.D. (DIFS FIT BUT), člen
Milet Tomáš, Ing., Ph.D. (DCGM FIT BUT), člen
@mastersthesis{FITMT24947,
author = "Tereza S\'{y}korov\'{a}",
type = "Master's thesis",
title = "Automatick\'{a} anal\'{y}za obrazu pro kontrolu kvality v\'{y}roby textili\'{i}",
school = "Brno University of Technology, Faculty of Information Technology",
year = 2022,
location = "Brno, CZ",
language = "czech",
url = "https://www.fit.vut.cz/study/thesis/24947/"
}