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A Versatile Machine Vision Algorithm for Real-Time Counting Manually Assembled Pieces

The Industry 4.0 paradigm is based on transparency and co-operation and, hence, on monitoring and pervasive data collection. In highly standardized contexts, it is usually easy to gather data using available technologies, while, in complex environments, only very advanced and customizable technologi...

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Detalles Bibliográficos
Autores principales: Pierleoni, Paola, Belli, Alberto, Palma, Lorenzo, Sabbatini, Luisiana
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8321050/
https://www.ncbi.nlm.nih.gov/pubmed/34460594
http://dx.doi.org/10.3390/jimaging6060048
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author Pierleoni, Paola
Belli, Alberto
Palma, Lorenzo
Sabbatini, Luisiana
author_facet Pierleoni, Paola
Belli, Alberto
Palma, Lorenzo
Sabbatini, Luisiana
author_sort Pierleoni, Paola
collection PubMed
description The Industry 4.0 paradigm is based on transparency and co-operation and, hence, on monitoring and pervasive data collection. In highly standardized contexts, it is usually easy to gather data using available technologies, while, in complex environments, only very advanced and customizable technologies, such as Computer Vision, are intelligent enough to perform such monitoring tasks well. By the term “complex environment”, we especially refer to those contexts where human activity which cannot be fully standardized prevails. In this work, we present a Machine Vision algorithm which is able to effectively deal with human interactions inside a framed area. By exploiting inter-frame analysis, image pre-processing, binarization, morphological operations, and blob detection, our solution is able to count the pieces assembled by an operator using a real-time video input. The solution is compared with a more advanced Machine Learning-based custom object detector, which is taken as reference. The proposed solution demonstrates a very good performance in terms of Sensitivity, Specificity, and Accuracy when tested on a real situation in an Italian manufacturing firm. The value of our solution, compared with the reference object detector, is that it requires no training and is therefore extremely flexible, requiring only minor changes to the working parameters to translate to other objects, making it appropriate for plant-wide implementation.
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spelling pubmed-83210502021-08-26 A Versatile Machine Vision Algorithm for Real-Time Counting Manually Assembled Pieces Pierleoni, Paola Belli, Alberto Palma, Lorenzo Sabbatini, Luisiana J Imaging Article The Industry 4.0 paradigm is based on transparency and co-operation and, hence, on monitoring and pervasive data collection. In highly standardized contexts, it is usually easy to gather data using available technologies, while, in complex environments, only very advanced and customizable technologies, such as Computer Vision, are intelligent enough to perform such monitoring tasks well. By the term “complex environment”, we especially refer to those contexts where human activity which cannot be fully standardized prevails. In this work, we present a Machine Vision algorithm which is able to effectively deal with human interactions inside a framed area. By exploiting inter-frame analysis, image pre-processing, binarization, morphological operations, and blob detection, our solution is able to count the pieces assembled by an operator using a real-time video input. The solution is compared with a more advanced Machine Learning-based custom object detector, which is taken as reference. The proposed solution demonstrates a very good performance in terms of Sensitivity, Specificity, and Accuracy when tested on a real situation in an Italian manufacturing firm. The value of our solution, compared with the reference object detector, is that it requires no training and is therefore extremely flexible, requiring only minor changes to the working parameters to translate to other objects, making it appropriate for plant-wide implementation. MDPI 2020-06-13 /pmc/articles/PMC8321050/ /pubmed/34460594 http://dx.doi.org/10.3390/jimaging6060048 Text en © 2020 by the authors. https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) ).
spellingShingle Article
Pierleoni, Paola
Belli, Alberto
Palma, Lorenzo
Sabbatini, Luisiana
A Versatile Machine Vision Algorithm for Real-Time Counting Manually Assembled Pieces
title A Versatile Machine Vision Algorithm for Real-Time Counting Manually Assembled Pieces
title_full A Versatile Machine Vision Algorithm for Real-Time Counting Manually Assembled Pieces
title_fullStr A Versatile Machine Vision Algorithm for Real-Time Counting Manually Assembled Pieces
title_full_unstemmed A Versatile Machine Vision Algorithm for Real-Time Counting Manually Assembled Pieces
title_short A Versatile Machine Vision Algorithm for Real-Time Counting Manually Assembled Pieces
title_sort versatile machine vision algorithm for real-time counting manually assembled pieces
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8321050/
https://www.ncbi.nlm.nih.gov/pubmed/34460594
http://dx.doi.org/10.3390/jimaging6060048
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