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Quality Control of PET Bottles Caps with Dedicated Image Calibration and Deep Neural Networks

Product quality control is currently the leading trend in industrial production. It is heading towards the exact analysis of each product before reaching the end customer. Every stage of production control is of particular importance in the food and pharmaceutical industries, where, apart from visua...

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Detalles Bibliográficos
Autores principales: Malesa, Marcin, Rajkiewicz, Piotr
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7827049/
https://www.ncbi.nlm.nih.gov/pubmed/33445641
http://dx.doi.org/10.3390/s21020501
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author Malesa, Marcin
Rajkiewicz, Piotr
author_facet Malesa, Marcin
Rajkiewicz, Piotr
author_sort Malesa, Marcin
collection PubMed
description Product quality control is currently the leading trend in industrial production. It is heading towards the exact analysis of each product before reaching the end customer. Every stage of production control is of particular importance in the food and pharmaceutical industries, where, apart from visual issues, additional safety regulations are demanded. Many production processes can be controlled completely contactless through the use of machine vision cameras and advanced image processing techniques. The most dynamically growing sector of image analysis methods are solutions based on deep neural networks. Their major advantages are fast performance, robustness, and the fact that they can be exploited even in complicated classification problems. However, the use of machine learning methods on high-performance production lines may be limited by inference time or, in the case of multiformated production lines, training time. The article presents a novel data preprocessing (or calibration) method. It uses prior knowledge about the optical system, which enables the use of the lightweight Convolutional Neural Network (CNN) model for product quality control of polyethylene terephthalate (PET) bottle caps. The combination of preprocessing with the lightweight CNN model resulted in at least a five-fold reduction in prediction and training time compared to the lighter standard models tested on ImageNet, without loss of accuracy.
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spelling pubmed-78270492021-01-25 Quality Control of PET Bottles Caps with Dedicated Image Calibration and Deep Neural Networks Malesa, Marcin Rajkiewicz, Piotr Sensors (Basel) Article Product quality control is currently the leading trend in industrial production. It is heading towards the exact analysis of each product before reaching the end customer. Every stage of production control is of particular importance in the food and pharmaceutical industries, where, apart from visual issues, additional safety regulations are demanded. Many production processes can be controlled completely contactless through the use of machine vision cameras and advanced image processing techniques. The most dynamically growing sector of image analysis methods are solutions based on deep neural networks. Their major advantages are fast performance, robustness, and the fact that they can be exploited even in complicated classification problems. However, the use of machine learning methods on high-performance production lines may be limited by inference time or, in the case of multiformated production lines, training time. The article presents a novel data preprocessing (or calibration) method. It uses prior knowledge about the optical system, which enables the use of the lightweight Convolutional Neural Network (CNN) model for product quality control of polyethylene terephthalate (PET) bottle caps. The combination of preprocessing with the lightweight CNN model resulted in at least a five-fold reduction in prediction and training time compared to the lighter standard models tested on ImageNet, without loss of accuracy. MDPI 2021-01-12 /pmc/articles/PMC7827049/ /pubmed/33445641 http://dx.doi.org/10.3390/s21020501 Text en © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Malesa, Marcin
Rajkiewicz, Piotr
Quality Control of PET Bottles Caps with Dedicated Image Calibration and Deep Neural Networks
title Quality Control of PET Bottles Caps with Dedicated Image Calibration and Deep Neural Networks
title_full Quality Control of PET Bottles Caps with Dedicated Image Calibration and Deep Neural Networks
title_fullStr Quality Control of PET Bottles Caps with Dedicated Image Calibration and Deep Neural Networks
title_full_unstemmed Quality Control of PET Bottles Caps with Dedicated Image Calibration and Deep Neural Networks
title_short Quality Control of PET Bottles Caps with Dedicated Image Calibration and Deep Neural Networks
title_sort quality control of pet bottles caps with dedicated image calibration and deep neural networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7827049/
https://www.ncbi.nlm.nih.gov/pubmed/33445641
http://dx.doi.org/10.3390/s21020501
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