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The Detection of Thread Roll’s Margin Based on Computer Vision

The automatic detection of the thread roll’s margin is one of the kernel problems in the textile field. As the traditional detection method based on the thread’s tension has the disadvantages of high cost and low reliability, this paper proposes a technology that installs a camera on a mobile robot...

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Detalles Bibliográficos
Autores principales: Shi, Zhiwei, Shi, Weimin, Wang, Junru
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8512785/
https://www.ncbi.nlm.nih.gov/pubmed/34640651
http://dx.doi.org/10.3390/s21196331
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author Shi, Zhiwei
Shi, Weimin
Wang, Junru
author_facet Shi, Zhiwei
Shi, Weimin
Wang, Junru
author_sort Shi, Zhiwei
collection PubMed
description The automatic detection of the thread roll’s margin is one of the kernel problems in the textile field. As the traditional detection method based on the thread’s tension has the disadvantages of high cost and low reliability, this paper proposes a technology that installs a camera on a mobile robot and uses computer vision to detect the thread roll‘s margin. Before starting, we define a thread roll‘s margin as follows: The difference between the thread roll‘s radius and the bobbin’s radius. Firstly, we capture images of the thread roll‘s end surface. Secondly, we obtain the bobbin’s image coordinates by calculating the image’s convolutions with a Circle Gradient Operator. Thirdly, we fit the thread roll and bobbin’s contours into ellipses, and then delete false detections according to the bobbin’s image coordinates. Finally, we restore every sub-image of the thread roll by a perspective transformation method, and establish the conversion relationship between the actual size and pixel size. The difference value of the two concentric circles’ radii is the thread roll’s margin. However, there are false detections and these errors may be more than 19.4 mm when the margin is small. In order to improve the precision and delete false detections, we use deep learning to detect thread roll and bobbin’s radii and then can calculate the thread roll’s margin. After that, we fuse the two results. However, the deep learning method also has some false detections. As such, in order to eliminate the false detections completely, we estimate the thread roll‘s margin according to thread consumption speed. Lastly, we use a Kalman Filter to fuse the measured value and estimated value; the average error is less than 5.7 mm.
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spelling pubmed-85127852021-10-14 The Detection of Thread Roll’s Margin Based on Computer Vision Shi, Zhiwei Shi, Weimin Wang, Junru Sensors (Basel) Article The automatic detection of the thread roll’s margin is one of the kernel problems in the textile field. As the traditional detection method based on the thread’s tension has the disadvantages of high cost and low reliability, this paper proposes a technology that installs a camera on a mobile robot and uses computer vision to detect the thread roll‘s margin. Before starting, we define a thread roll‘s margin as follows: The difference between the thread roll‘s radius and the bobbin’s radius. Firstly, we capture images of the thread roll‘s end surface. Secondly, we obtain the bobbin’s image coordinates by calculating the image’s convolutions with a Circle Gradient Operator. Thirdly, we fit the thread roll and bobbin’s contours into ellipses, and then delete false detections according to the bobbin’s image coordinates. Finally, we restore every sub-image of the thread roll by a perspective transformation method, and establish the conversion relationship between the actual size and pixel size. The difference value of the two concentric circles’ radii is the thread roll’s margin. However, there are false detections and these errors may be more than 19.4 mm when the margin is small. In order to improve the precision and delete false detections, we use deep learning to detect thread roll and bobbin’s radii and then can calculate the thread roll’s margin. After that, we fuse the two results. However, the deep learning method also has some false detections. As such, in order to eliminate the false detections completely, we estimate the thread roll‘s margin according to thread consumption speed. Lastly, we use a Kalman Filter to fuse the measured value and estimated value; the average error is less than 5.7 mm. MDPI 2021-09-22 /pmc/articles/PMC8512785/ /pubmed/34640651 http://dx.doi.org/10.3390/s21196331 Text en © 2021 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
Shi, Zhiwei
Shi, Weimin
Wang, Junru
The Detection of Thread Roll’s Margin Based on Computer Vision
title The Detection of Thread Roll’s Margin Based on Computer Vision
title_full The Detection of Thread Roll’s Margin Based on Computer Vision
title_fullStr The Detection of Thread Roll’s Margin Based on Computer Vision
title_full_unstemmed The Detection of Thread Roll’s Margin Based on Computer Vision
title_short The Detection of Thread Roll’s Margin Based on Computer Vision
title_sort detection of thread roll’s margin based on computer vision
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8512785/
https://www.ncbi.nlm.nih.gov/pubmed/34640651
http://dx.doi.org/10.3390/s21196331
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