No Cover Image

Journal article 105 views 33 downloads

Portable IoT device for tire text code identification via integrated computer vision system

Haowei Zhang, Kang Gao, Yue Hou Orcid Logo, Marco Domaneschi, Mohammad Noori

Computer-Aided Civil and Infrastructure Engineering

Swansea University Author: Yue Hou Orcid Logo

  • 68791.VOR.pdf

    PDF | Version of Record

    © 2025 The Author(s). This is an open access article under the terms of the Creative Commons Attribution License.

    Download (4.34MB)

Check full text

DOI (Published version): 10.1111/mice.13438

Abstract

The identification of tire text codes (TTC) during the production and operational phases of tires can significantly improve safety and maintenance practices. Current methods for TTC identification face challenges related to stability, computational efficiency, and outdoor applicability. This paper i...

Full description

Published in: Computer-Aided Civil and Infrastructure Engineering
ISSN: 1093-9687 1467-8667
Published: Wiley 2025
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa68791
first_indexed 2025-02-04T10:23:23Z
last_indexed 2025-03-28T06:15:25Z
id cronfa68791
recordtype SURis
fullrecord <?xml version="1.0"?><rfc1807><datestamp>2025-03-27T12:30:13.7235645</datestamp><bib-version>v2</bib-version><id>68791</id><entry>2025-02-04</entry><title>Portable IoT device for tire text code identification via integrated computer vision system</title><swanseaauthors><author><sid>92bf566c65343cb3ee04ad963eacf31b</sid><ORCID>0000-0002-4334-2620</ORCID><firstname>Yue</firstname><surname>Hou</surname><name>Yue Hou</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2025-02-04</date><deptcode>ACEM</deptcode><abstract>The identification of tire text codes (TTC) during the production and operational phases of tires can significantly improve safety and maintenance practices. Current methods for TTC identification face challenges related to stability, computational efficiency, and outdoor applicability. This paper introduces an automated TTC identification system founded on a robust framework that is both user-friendly and easy to implement, thereby enhancing the practical use and industrial applicability of TTC identification technologies. Initially, instance segmentation is creatively utilized for detecting TTC regions on the tire sidewall through You Only Look Once (YOLO)-v8-based models, which are trained on a dataset comprising 430 real-world tire images. Subsequently, a computationally efficient rotation algorithm, along with specific image pre-processing techniques, is developed to tackle common issues associated with centripetal rotation in the TTC region and to improve the accuracy of TTC region detection. Furthermore, a series of YOLO-v8 object detection models were assessed using an independently collected dataset of 1127 images to optimize the recognition of TTC characters. Ultimately, a portable Internet of Things (IoT) vision device is created, featuring a comprehensive workflow to support the proposed TTC identification framework. The TTC region detection model achieves a segmentation precision of 0.8812, while the TTC recognition model reaches a precision of 0.9710, based on the datasets presented in this paper. Field tests demonstrate the system's advancements, reliability, and potential industrial significance for practical applications. The IoT device is shown to be portable, cost-effective, and capable of processing each tire in 200 ms.</abstract><type>Journal Article</type><journal>Computer-Aided Civil and Infrastructure Engineering</journal><volume>0</volume><journalNumber/><paginationStart/><paginationEnd/><publisher>Wiley</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint>1093-9687</issnPrint><issnElectronic>1467-8667</issnElectronic><keywords/><publishedDay>13</publishedDay><publishedMonth>2</publishedMonth><publishedYear>2025</publishedYear><publishedDate>2025-02-13</publishedDate><doi>10.1111/mice.13438</doi><url/><notes/><college>COLLEGE NANME</college><department>Aerospace, Civil, Electrical, and Mechanical Engineering</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>ACEM</DepartmentCode><institution>Swansea University</institution><apcterm>SU Library paid the OA fee (TA Institutional Deal)</apcterm><funders>National Natural Science Foundation of China (GrantNumber(s): 52208151, 52127813; Grant recipient(s): Gao Kang); Fundamental Research Funds for the Central Universities (GrantNumber(s): 2242023K5006; Grant recipient(s): Gao Kang)</funders><projectreference/><lastEdited>2025-03-27T12:30:13.7235645</lastEdited><Created>2025-02-04T10:17:57.9433595</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering</level></path><authors><author><firstname>Haowei</firstname><surname>Zhang</surname><order>1</order></author><author><firstname>Kang</firstname><surname>Gao</surname><order>2</order></author><author><firstname>Yue</firstname><surname>Hou</surname><orcid>0000-0002-4334-2620</orcid><order>3</order></author><author><firstname>Marco</firstname><surname>Domaneschi</surname><order>4</order></author><author><firstname>Mohammad</firstname><surname>Noori</surname><order>5</order></author></authors><documents><document><filename>68791__33616__a7bc85cfabbd488aad1c93323f6e2cc5.pdf</filename><originalFilename>68791.VOR.pdf</originalFilename><uploaded>2025-02-18T11:27:08.6939406</uploaded><type>Output</type><contentLength>4546291</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>&#xA9; 2025 The Author(s). This is an open access article under the terms of the Creative Commons Attribution License.</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language><licence>http://creativecommons.org/licenses/by/4.0/</licence></document></documents><OutputDurs/></rfc1807>
spelling 2025-03-27T12:30:13.7235645 v2 68791 2025-02-04 Portable IoT device for tire text code identification via integrated computer vision system 92bf566c65343cb3ee04ad963eacf31b 0000-0002-4334-2620 Yue Hou Yue Hou true false 2025-02-04 ACEM The identification of tire text codes (TTC) during the production and operational phases of tires can significantly improve safety and maintenance practices. Current methods for TTC identification face challenges related to stability, computational efficiency, and outdoor applicability. This paper introduces an automated TTC identification system founded on a robust framework that is both user-friendly and easy to implement, thereby enhancing the practical use and industrial applicability of TTC identification technologies. Initially, instance segmentation is creatively utilized for detecting TTC regions on the tire sidewall through You Only Look Once (YOLO)-v8-based models, which are trained on a dataset comprising 430 real-world tire images. Subsequently, a computationally efficient rotation algorithm, along with specific image pre-processing techniques, is developed to tackle common issues associated with centripetal rotation in the TTC region and to improve the accuracy of TTC region detection. Furthermore, a series of YOLO-v8 object detection models were assessed using an independently collected dataset of 1127 images to optimize the recognition of TTC characters. Ultimately, a portable Internet of Things (IoT) vision device is created, featuring a comprehensive workflow to support the proposed TTC identification framework. The TTC region detection model achieves a segmentation precision of 0.8812, while the TTC recognition model reaches a precision of 0.9710, based on the datasets presented in this paper. Field tests demonstrate the system's advancements, reliability, and potential industrial significance for practical applications. The IoT device is shown to be portable, cost-effective, and capable of processing each tire in 200 ms. Journal Article Computer-Aided Civil and Infrastructure Engineering 0 Wiley 1093-9687 1467-8667 13 2 2025 2025-02-13 10.1111/mice.13438 COLLEGE NANME Aerospace, Civil, Electrical, and Mechanical Engineering COLLEGE CODE ACEM Swansea University SU Library paid the OA fee (TA Institutional Deal) National Natural Science Foundation of China (GrantNumber(s): 52208151, 52127813; Grant recipient(s): Gao Kang); Fundamental Research Funds for the Central Universities (GrantNumber(s): 2242023K5006; Grant recipient(s): Gao Kang) 2025-03-27T12:30:13.7235645 2025-02-04T10:17:57.9433595 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering Haowei Zhang 1 Kang Gao 2 Yue Hou 0000-0002-4334-2620 3 Marco Domaneschi 4 Mohammad Noori 5 68791__33616__a7bc85cfabbd488aad1c93323f6e2cc5.pdf 68791.VOR.pdf 2025-02-18T11:27:08.6939406 Output 4546291 application/pdf Version of Record true © 2025 The Author(s). This is an open access article under the terms of the Creative Commons Attribution License. true eng http://creativecommons.org/licenses/by/4.0/
title Portable IoT device for tire text code identification via integrated computer vision system
spellingShingle Portable IoT device for tire text code identification via integrated computer vision system
Yue Hou
title_short Portable IoT device for tire text code identification via integrated computer vision system
title_full Portable IoT device for tire text code identification via integrated computer vision system
title_fullStr Portable IoT device for tire text code identification via integrated computer vision system
title_full_unstemmed Portable IoT device for tire text code identification via integrated computer vision system
title_sort Portable IoT device for tire text code identification via integrated computer vision system
author_id_str_mv 92bf566c65343cb3ee04ad963eacf31b
author_id_fullname_str_mv 92bf566c65343cb3ee04ad963eacf31b_***_Yue Hou
author Yue Hou
author2 Haowei Zhang
Kang Gao
Yue Hou
Marco Domaneschi
Mohammad Noori
format Journal article
container_title Computer-Aided Civil and Infrastructure Engineering
container_volume 0
publishDate 2025
institution Swansea University
issn 1093-9687
1467-8667
doi_str_mv 10.1111/mice.13438
publisher Wiley
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering
document_store_str 1
active_str 0
description The identification of tire text codes (TTC) during the production and operational phases of tires can significantly improve safety and maintenance practices. Current methods for TTC identification face challenges related to stability, computational efficiency, and outdoor applicability. This paper introduces an automated TTC identification system founded on a robust framework that is both user-friendly and easy to implement, thereby enhancing the practical use and industrial applicability of TTC identification technologies. Initially, instance segmentation is creatively utilized for detecting TTC regions on the tire sidewall through You Only Look Once (YOLO)-v8-based models, which are trained on a dataset comprising 430 real-world tire images. Subsequently, a computationally efficient rotation algorithm, along with specific image pre-processing techniques, is developed to tackle common issues associated with centripetal rotation in the TTC region and to improve the accuracy of TTC region detection. Furthermore, a series of YOLO-v8 object detection models were assessed using an independently collected dataset of 1127 images to optimize the recognition of TTC characters. Ultimately, a portable Internet of Things (IoT) vision device is created, featuring a comprehensive workflow to support the proposed TTC identification framework. The TTC region detection model achieves a segmentation precision of 0.8812, while the TTC recognition model reaches a precision of 0.9710, based on the datasets presented in this paper. Field tests demonstrate the system's advancements, reliability, and potential industrial significance for practical applications. The IoT device is shown to be portable, cost-effective, and capable of processing each tire in 200 ms.
published_date 2025-02-13T09:39:36Z
_version_ 1830272605744005120
score 11.060726

Similar Items