Tyre Inspection through Multi-State Convolutional Neural Networks
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Sivamani, C. | - |
dc.contributor.author | Rajeswari, M. | - |
dc.contributor.author | Julie, E. Golden | - |
dc.contributor.author | Robinson, Y. Harold | - |
dc.contributor.author | Shanmuganathan, Vimal | - |
dc.contributor.author | Kadry, Seifedine | - |
dc.contributor.author | Nam, Yunyoung | - |
dc.date.accessioned | 2021-08-11T08:31:12Z | - |
dc.date.available | 2021-08-11T08:31:12Z | - |
dc.date.issued | 2021 | - |
dc.identifier.issn | 1079-8587 | - |
dc.identifier.issn | 2326-005X | - |
dc.identifier.uri | https://scholarworks.bwise.kr/sch/handle/2021.sw.sch/2202 | - |
dc.description.abstract | Road accident is a potential risk to the lives of both drivers and passersby. Many road accidents occur due to the improper condition of the vehicle tyres after long term usage. Thus, tyres need to be inspected and analyzed while manufacturing to avoid serious road problems. However, tyre wear is a multifaceted happening. It normally needs the non-linearly on many limitations, like tyre formation and plan, vehicle category, conditions of the road. Yet, tyre wear has numerous profitable and environmental inferences particularly due to maintenance costs and traffic safety implications. Thus, the risk to calculate tyre wear is therefore of major importance to tyre producers, convoy owners and government. In this paper, we propose a Multi-state Convolution Neural Networks to analyze tyre tread patterns about wear and tear as well as tyre durability. The feature maps are identified from the input image through the Convolution functions that the sub-sampling utilizes for producing the output with the fully connected networks. The quadratic surface uses to perform the preprocessing of tyre images with several Convolutional layers. Through this work, we aim to reduce the economic implications as well as traffic safety implications which happen due to tyre wear. This will serve as a potential solution to tyre wear-related issues. | - |
dc.format.extent | 13 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | AutoSoft Press | - |
dc.title | Tyre Inspection through Multi-State Convolutional Neural Networks | - |
dc.type | Article | - |
dc.publisher.location | 미국 | - |
dc.identifier.doi | 10.32604/iasc.2021.013705 | - |
dc.identifier.scopusid | 2-s2.0-85100093395 | - |
dc.identifier.wosid | 000624947600001 | - |
dc.identifier.bibliographicCitation | Intelligent Automation and Soft Computing, v.27, no.1, pp 1 - 13 | - |
dc.citation.title | Intelligent Automation and Soft Computing | - |
dc.citation.volume | 27 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 1 | - |
dc.citation.endPage | 13 | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Automation & Control Systems | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Automation & Control Systems | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.subject.keywordAuthor | Convolutional neural networks | - |
dc.subject.keywordAuthor | machine learning | - |
dc.subject.keywordAuthor | deep learning | - |
dc.subject.keywordAuthor | tyre wear prediction | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(31538) 22, Soonchunhyang-ro, Asan-si, Chungcheongnam-do, Republic of Korea+82-41-530-1114
COPYRIGHT 2021 by SOONCHUNHYANG UNIVERSITY ALL RIGHTS RESERVED.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.