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Research on Tire Marking Point Completeness Evaluation Based on K-Means Clustering Image Segmentation
The tire marking points of dynamic balance and uniformity play a crucial guiding role in tire installation. Incomplete marking points block the recognition of tire marking points, and then affect the installation of tires. It is usually necessary to evaluate the marking point completeness during the...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7506796/ https://www.ncbi.nlm.nih.gov/pubmed/32825149 http://dx.doi.org/10.3390/s20174687 |
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author | Yu, Yuan Ren, Jinsheng Zhang, Qi Yang, Weimin Jiao, Zhiwei |
author_facet | Yu, Yuan Ren, Jinsheng Zhang, Qi Yang, Weimin Jiao, Zhiwei |
author_sort | Yu, Yuan |
collection | PubMed |
description | The tire marking points of dynamic balance and uniformity play a crucial guiding role in tire installation. Incomplete marking points block the recognition of tire marking points, and then affect the installation of tires. It is usually necessary to evaluate the marking point completeness during the quality inspection of finished tires. In order to meet the high-precision requirements of the evaluation of tire marking point completeness in the smart factories, the K-means clustering algorithm is introduced to segment the image of marking points in this paper. The pixels within the contour of the marking point are weighted to calculate the marking point completeness on the basis of the image segmentation. The completeness is rated and evaluated by completeness calculation. The experimental results show that the accuracy of the marking point completeness ratings is 95%, and the accuracy of the marking point evaluations is 99%. The proposed method has an important guiding significance of practice to evaluate the tire marking point completeness during the tire quality inspection based on machine vision. |
format | Online Article Text |
id | pubmed-7506796 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-75067962020-09-26 Research on Tire Marking Point Completeness Evaluation Based on K-Means Clustering Image Segmentation Yu, Yuan Ren, Jinsheng Zhang, Qi Yang, Weimin Jiao, Zhiwei Sensors (Basel) Article The tire marking points of dynamic balance and uniformity play a crucial guiding role in tire installation. Incomplete marking points block the recognition of tire marking points, and then affect the installation of tires. It is usually necessary to evaluate the marking point completeness during the quality inspection of finished tires. In order to meet the high-precision requirements of the evaluation of tire marking point completeness in the smart factories, the K-means clustering algorithm is introduced to segment the image of marking points in this paper. The pixels within the contour of the marking point are weighted to calculate the marking point completeness on the basis of the image segmentation. The completeness is rated and evaluated by completeness calculation. The experimental results show that the accuracy of the marking point completeness ratings is 95%, and the accuracy of the marking point evaluations is 99%. The proposed method has an important guiding significance of practice to evaluate the tire marking point completeness during the tire quality inspection based on machine vision. MDPI 2020-08-19 /pmc/articles/PMC7506796/ /pubmed/32825149 http://dx.doi.org/10.3390/s20174687 Text en © 2020 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 Yu, Yuan Ren, Jinsheng Zhang, Qi Yang, Weimin Jiao, Zhiwei Research on Tire Marking Point Completeness Evaluation Based on K-Means Clustering Image Segmentation |
title | Research on Tire Marking Point Completeness Evaluation Based on K-Means Clustering Image Segmentation |
title_full | Research on Tire Marking Point Completeness Evaluation Based on K-Means Clustering Image Segmentation |
title_fullStr | Research on Tire Marking Point Completeness Evaluation Based on K-Means Clustering Image Segmentation |
title_full_unstemmed | Research on Tire Marking Point Completeness Evaluation Based on K-Means Clustering Image Segmentation |
title_short | Research on Tire Marking Point Completeness Evaluation Based on K-Means Clustering Image Segmentation |
title_sort | research on tire marking point completeness evaluation based on k-means clustering image segmentation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7506796/ https://www.ncbi.nlm.nih.gov/pubmed/32825149 http://dx.doi.org/10.3390/s20174687 |
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