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
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CNN model architecture created by VGG (Visual Geometry Group, University of Oxford) for the ILSVRC-2014 contest.
MobileNetV1 from Google is a CNN model particularly useful for mobile and embedded vision applications.
CNN model that is the first runner up for image classification in ILSVRC-15.
CNN model that won the first place in the ILSVRC-15 classification competition with top-5 error rate of 3.57%
State of the art CNN model architecture combining ResNet and Inception features.
The CIFAR-10 dataset (Canadian Institute For Advanced Research) is a collection of images that are commonly used to train machine learning and computer vision algorithms.
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Funding
This research has been supported by Portuguese National funds through FITEC - Programa Interface, with reference CIT “INOV - INESC Inovação - Financiamento Base”.
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San-Payo, G., Ferreira, J.C., Santos, P. et al. Machine learning for quality control system. J Ambient Intell Human Comput 11, 4491–4500 (2020). https://doi.org/10.1007/s12652-019-01640-4
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DOI: https://doi.org/10.1007/s12652-019-01640-4