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Title: Machine learning for quality control system
Authors: San-Payo, G.
Ferreira, J.
Santos, P.
Martins, A.
Keywords: Quality control
Incremental learning
Image classification
Defect detection system
Issue Date: 2020
Publisher: Springer
Abstract: In this work, we propose and develop a classification model to be used in a quality control system for clothing manufacturing using machine learning algorithms. The system consists of using pictures taken through mobile devices to detect defects on production objects. In this work, a defect can be a missing component or a wrong component in a production object. Therefore, the function of the system is to classify the components that compose a production object through the use of a classification model. As a manufacturing business progresses, new objects are created, thus, the classification model must be able to learn the new classes without losing previous knowledge. However, most classification algorithms do not support an increase of classes, these need to be trained from scratch with all . Thus. In this work, we make use of an incremental learning algorithm to tackle this problem. This algorithm classifies features extracted from pictures of the production objects using a convolutional neural network (CNN), which have proven to be very successful in image classification problems. We apply the current developed approach to a process in clothing manufacturing. Therefore, the production objects correspond to clothing items
Peer reviewed: yes
DOI: 10.1007/s12652-019-01640-4
ISSN: 1868-5137
Accession number: WOS:000574435900004
Appears in Collections:BRU-RI - Artigos em revistas científicas internacionais com arbitragem científica
ISTAR-RI - Artigos em revistas científicas internacionais com arbitragem científica

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