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A Numerical Measurement Method for Dynamic Granular Materials Based on Computer Vision
Granular materials are widespread in nature and human production, and their macro-mechanical behavior is significantly affected by granule movement. The development of computer vision has brought some new ideas for measuring the numerical information (including the amount of translation, the rotatio...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9148117/ https://www.ncbi.nlm.nih.gov/pubmed/35629581 http://dx.doi.org/10.3390/ma15103554 |
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author | Liu, Hao Nie, Yuxing Chen, Man Liu, Shunkai Mohammed, Ashiru |
author_facet | Liu, Hao Nie, Yuxing Chen, Man Liu, Shunkai Mohammed, Ashiru |
author_sort | Liu, Hao |
collection | PubMed |
description | Granular materials are widespread in nature and human production, and their macro-mechanical behavior is significantly affected by granule movement. The development of computer vision has brought some new ideas for measuring the numerical information (including the amount of translation, the rotation angle, velocity, acceleration, etc.) of dynamic granular materials. In this paper, we propose a numerical measurement method for dynamic granular materials based on computer vision. Firstly, an improved video instance segmentation (VIS) network is introduced to perform end-to-end multi-task learning, and its temporal feature fusion module and tracking head with long-sequence external memory can improve the problems of poor video data quality and high similarity in appearance of granular materials, respectively. Secondly, the numerical information can be extracted through a series of post-processing steps. Finally, the effectiveness of the measurement method is verified by comparing the numerical measurement results with the real values. The experimental results indicate that our improved VIS obtains an average precision (AP) of 76.6, the relative errors and standard deviations are maintained at a low level, and this method can effectively be used to measure the numerical information of dynamic granular materials. This study provides an intelligent proposal for the task of measuring numerical information of dynamic granular materials, which is of great significance for studying the spatial distribution, motion mode and macro-mechanical behavior of granular materials. |
format | Online Article Text |
id | pubmed-9148117 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-91481172022-05-29 A Numerical Measurement Method for Dynamic Granular Materials Based on Computer Vision Liu, Hao Nie, Yuxing Chen, Man Liu, Shunkai Mohammed, Ashiru Materials (Basel) Article Granular materials are widespread in nature and human production, and their macro-mechanical behavior is significantly affected by granule movement. The development of computer vision has brought some new ideas for measuring the numerical information (including the amount of translation, the rotation angle, velocity, acceleration, etc.) of dynamic granular materials. In this paper, we propose a numerical measurement method for dynamic granular materials based on computer vision. Firstly, an improved video instance segmentation (VIS) network is introduced to perform end-to-end multi-task learning, and its temporal feature fusion module and tracking head with long-sequence external memory can improve the problems of poor video data quality and high similarity in appearance of granular materials, respectively. Secondly, the numerical information can be extracted through a series of post-processing steps. Finally, the effectiveness of the measurement method is verified by comparing the numerical measurement results with the real values. The experimental results indicate that our improved VIS obtains an average precision (AP) of 76.6, the relative errors and standard deviations are maintained at a low level, and this method can effectively be used to measure the numerical information of dynamic granular materials. This study provides an intelligent proposal for the task of measuring numerical information of dynamic granular materials, which is of great significance for studying the spatial distribution, motion mode and macro-mechanical behavior of granular materials. MDPI 2022-05-16 /pmc/articles/PMC9148117/ /pubmed/35629581 http://dx.doi.org/10.3390/ma15103554 Text en © 2022 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 (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Liu, Hao Nie, Yuxing Chen, Man Liu, Shunkai Mohammed, Ashiru A Numerical Measurement Method for Dynamic Granular Materials Based on Computer Vision |
title | A Numerical Measurement Method for Dynamic Granular Materials Based on Computer Vision |
title_full | A Numerical Measurement Method for Dynamic Granular Materials Based on Computer Vision |
title_fullStr | A Numerical Measurement Method for Dynamic Granular Materials Based on Computer Vision |
title_full_unstemmed | A Numerical Measurement Method for Dynamic Granular Materials Based on Computer Vision |
title_short | A Numerical Measurement Method for Dynamic Granular Materials Based on Computer Vision |
title_sort | numerical measurement method for dynamic granular materials based on computer vision |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9148117/ https://www.ncbi.nlm.nih.gov/pubmed/35629581 http://dx.doi.org/10.3390/ma15103554 |
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