Deep learning for the quality control of thermoforming food packages

Abstract Quality control is a key process designed to ensure that only products satisfying the defined quality requirements reach the end consumer or the next step in a production line. In the food industry, in the packaging step, there are many products that are still evaluated by human operators....

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Autores principales:	Núria Banús, Imma Boada, Pau Xiberta, Pol Toldrà, Narcís Bustins
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/3506599635014ae2b57c2b856f1ebdca
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spelling	oai:doaj.org-article:3506599635014ae2b57c2b856f1ebdca2021-11-14T12:19:35ZDeep learning for the quality control of thermoforming food packages10.1038/s41598-021-01254-x2045-2322https://doaj.org/article/3506599635014ae2b57c2b856f1ebdca2021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-01254-xhttps://doaj.org/toc/2045-2322Abstract Quality control is a key process designed to ensure that only products satisfying the defined quality requirements reach the end consumer or the next step in a production line. In the food industry, in the packaging step, there are many products that are still evaluated by human operators. To automate the process and improve efficiency and effectiveness, computer vision and artificial intelligence techniques can be applied. This automation is challenging since specific strategies designed according to the application scenario are required. Focusing on the quality control of the sealing and closure of matrix-shaped thermoforming food packages, the aim of the article is to propose a deep-learning-based solution designed to automatically perform the quality control while satisfying production cadence and ensuring 100% inline inspection of the products. Particularly, the designed computer vision system and the image-based criteria defined to determine when a product has to be accepted or rejected are presented. In addition, the vision control software is described with special emphasis on the different convolutional neural network (CNN) architectures that have been considered (ResNet18, ResNet50, Vgg19 and DenseNet161, non-pre-trained and pre-trained on ImageNet) and on the specifically designed dataset. To test the solution, different experiments are carried out in the laboratory and also in a real scenario, concluding that the proposed CNN-based approach improves the efficiency and security of the quality control process. Optimal results are obtained with the pre-trained DenseNet161, achieving false positive rates that range from 0.03 to 0.30% and false negative rates that range from 0 to 0.07%, with a rejection rate between 0.64 and 5.09% of production, and being able to detect at least 99.93% of the sealing defects that occur in any production. The modular design of our solution as well as the provided description allow it to adapt to similar scenarios and to new deep-learning models to prevent the arrival of faulty products to end consumers by removing them from the automated production line.Núria BanúsImma BoadaPau XibertaPol ToldràNarcís BustinsNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Núria Banús Imma Boada Pau Xiberta Pol Toldrà Narcís Bustins Deep learning for the quality control of thermoforming food packages
description	Abstract Quality control is a key process designed to ensure that only products satisfying the defined quality requirements reach the end consumer or the next step in a production line. In the food industry, in the packaging step, there are many products that are still evaluated by human operators. To automate the process and improve efficiency and effectiveness, computer vision and artificial intelligence techniques can be applied. This automation is challenging since specific strategies designed according to the application scenario are required. Focusing on the quality control of the sealing and closure of matrix-shaped thermoforming food packages, the aim of the article is to propose a deep-learning-based solution designed to automatically perform the quality control while satisfying production cadence and ensuring 100% inline inspection of the products. Particularly, the designed computer vision system and the image-based criteria defined to determine when a product has to be accepted or rejected are presented. In addition, the vision control software is described with special emphasis on the different convolutional neural network (CNN) architectures that have been considered (ResNet18, ResNet50, Vgg19 and DenseNet161, non-pre-trained and pre-trained on ImageNet) and on the specifically designed dataset. To test the solution, different experiments are carried out in the laboratory and also in a real scenario, concluding that the proposed CNN-based approach improves the efficiency and security of the quality control process. Optimal results are obtained with the pre-trained DenseNet161, achieving false positive rates that range from 0.03 to 0.30% and false negative rates that range from 0 to 0.07%, with a rejection rate between 0.64 and 5.09% of production, and being able to detect at least 99.93% of the sealing defects that occur in any production. The modular design of our solution as well as the provided description allow it to adapt to similar scenarios and to new deep-learning models to prevent the arrival of faulty products to end consumers by removing them from the automated production line.
format	article
author	Núria Banús Imma Boada Pau Xiberta Pol Toldrà Narcís Bustins
author_facet	Núria Banús Imma Boada Pau Xiberta Pol Toldrà Narcís Bustins
author_sort	Núria Banús
title	Deep learning for the quality control of thermoforming food packages
title_short	Deep learning for the quality control of thermoforming food packages
title_full	Deep learning for the quality control of thermoforming food packages
title_fullStr	Deep learning for the quality control of thermoforming food packages
title_full_unstemmed	Deep learning for the quality control of thermoforming food packages
title_sort	deep learning for the quality control of thermoforming food packages
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/3506599635014ae2b57c2b856f1ebdca
work_keys_str_mv	AT nuriabanus deeplearningforthequalitycontrolofthermoformingfoodpackages AT immaboada deeplearningforthequalitycontrolofthermoformingfoodpackages AT pauxiberta deeplearningforthequalitycontrolofthermoformingfoodpackages AT poltoldra deeplearningforthequalitycontrolofthermoformingfoodpackages AT narcisbustins deeplearningforthequalitycontrolofthermoformingfoodpackages
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Deep learning for the quality control of thermoforming food packages

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