Digital images of defective and good condition tyres

The dataset contains 1854 digital tyres images, categorized into two classes: defective and good condition. Each image is in a digital format and represents a single tyre. The images are labelled based on their condition, i.e., whether the tyre is defective or in good condition. This dataset can be...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
1. Verfasser: Pathmanaban P
Format: Dataset
Sprache:eng
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue
container_start_page
container_title
container_volume
creator Pathmanaban P
description The dataset contains 1854 digital tyres images, categorized into two classes: defective and good condition. Each image is in a digital format and represents a single tyre. The images are labelled based on their condition, i.e., whether the tyre is defective or in good condition. This dataset can be used for various machine learning and computer vision applications, such as image classification and object detection. Researchers and practitioners in transportation, the automotive industry, and quality control can use this dataset to train and test their models to identify the condition of tyres from digital images. The dataset provides a valuable resource to develop and evaluate the performance of algorithms for the automatic detection of defective tyres. The dataset may also help improve the tyre industry's quality control process and reduce the chances of accidents due to faulty tyres. The availability of this dataset can facilitate the development of more accurate and efficient inspection systems for tyre production.
doi_str_mv 10.17632/bn7ch8tvyp
format Dataset
fullrecord <record><control><sourceid>datacite_PQ8</sourceid><recordid>TN_cdi_datacite_primary_10_17632_bn7ch8tvyp</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>10_17632_bn7ch8tvyp</sourcerecordid><originalsourceid>FETCH-datacite_primary_10_17632_bn7ch8tvyp3</originalsourceid><addsrcrecordid>eNpjYBA2NNAzNDczNtJPyjNPzrAoKass4GQwcclMzyxJzFHIzE1MTy1WyE9TSElNS00uySxLVUjMS1FIz89PUUjOz0vJLMnMz1MoqSxKLeZhYE1LzClO5YXS3Azabq4hzh66KYklicmZJanxBUVA84oq4w0N4sF2xiPsNCZNNQAX2zua</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>dataset</recordtype></control><display><type>dataset</type><title>Digital images of defective and good condition tyres</title><source>DataCite</source><creator>Pathmanaban P</creator><creatorcontrib>Pathmanaban P</creatorcontrib><description>The dataset contains 1854 digital tyres images, categorized into two classes: defective and good condition. Each image is in a digital format and represents a single tyre. The images are labelled based on their condition, i.e., whether the tyre is defective or in good condition. This dataset can be used for various machine learning and computer vision applications, such as image classification and object detection. Researchers and practitioners in transportation, the automotive industry, and quality control can use this dataset to train and test their models to identify the condition of tyres from digital images. The dataset provides a valuable resource to develop and evaluate the performance of algorithms for the automatic detection of defective tyres. The dataset may also help improve the tyre industry's quality control process and reduce the chances of accidents due to faulty tyres. The availability of this dataset can facilitate the development of more accurate and efficient inspection systems for tyre production.</description><identifier>DOI: 10.17632/bn7ch8tvyp</identifier><language>eng</language><publisher>Mendeley</publisher><creationdate>2023</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>780,1894</link.rule.ids><linktorsrc>$$Uhttps://commons.datacite.org/doi.org/10.17632/bn7ch8tvyp$$EView_record_in_DataCite.org$$FView_record_in_$$GDataCite.org$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>Pathmanaban P</creatorcontrib><title>Digital images of defective and good condition tyres</title><description>The dataset contains 1854 digital tyres images, categorized into two classes: defective and good condition. Each image is in a digital format and represents a single tyre. The images are labelled based on their condition, i.e., whether the tyre is defective or in good condition. This dataset can be used for various machine learning and computer vision applications, such as image classification and object detection. Researchers and practitioners in transportation, the automotive industry, and quality control can use this dataset to train and test their models to identify the condition of tyres from digital images. The dataset provides a valuable resource to develop and evaluate the performance of algorithms for the automatic detection of defective tyres. The dataset may also help improve the tyre industry's quality control process and reduce the chances of accidents due to faulty tyres. The availability of this dataset can facilitate the development of more accurate and efficient inspection systems for tyre production.</description><fulltext>true</fulltext><rsrctype>dataset</rsrctype><creationdate>2023</creationdate><recordtype>dataset</recordtype><sourceid>PQ8</sourceid><recordid>eNpjYBA2NNAzNDczNtJPyjNPzrAoKass4GQwcclMzyxJzFHIzE1MTy1WyE9TSElNS00uySxLVUjMS1FIz89PUUjOz0vJLMnMz1MoqSxKLeZhYE1LzClO5YXS3Azabq4hzh66KYklicmZJanxBUVA84oq4w0N4sF2xiPsNCZNNQAX2zua</recordid><startdate>20230822</startdate><enddate>20230822</enddate><creator>Pathmanaban P</creator><general>Mendeley</general><scope>DYCCY</scope><scope>PQ8</scope></search><sort><creationdate>20230822</creationdate><title>Digital images of defective and good condition tyres</title><author>Pathmanaban P</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-datacite_primary_10_17632_bn7ch8tvyp3</frbrgroupid><rsrctype>datasets</rsrctype><prefilter>datasets</prefilter><language>eng</language><creationdate>2023</creationdate><toplevel>online_resources</toplevel><creatorcontrib>Pathmanaban P</creatorcontrib><collection>DataCite (Open Access)</collection><collection>DataCite</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Pathmanaban P</au><format>book</format><genre>unknown</genre><ristype>DATA</ristype><title>Digital images of defective and good condition tyres</title><date>2023-08-22</date><risdate>2023</risdate><abstract>The dataset contains 1854 digital tyres images, categorized into two classes: defective and good condition. Each image is in a digital format and represents a single tyre. The images are labelled based on their condition, i.e., whether the tyre is defective or in good condition. This dataset can be used for various machine learning and computer vision applications, such as image classification and object detection. Researchers and practitioners in transportation, the automotive industry, and quality control can use this dataset to train and test their models to identify the condition of tyres from digital images. The dataset provides a valuable resource to develop and evaluate the performance of algorithms for the automatic detection of defective tyres. The dataset may also help improve the tyre industry's quality control process and reduce the chances of accidents due to faulty tyres. The availability of this dataset can facilitate the development of more accurate and efficient inspection systems for tyre production.</abstract><pub>Mendeley</pub><doi>10.17632/bn7ch8tvyp</doi><oa>free_for_read</oa></addata></record>
fulltext fulltext_linktorsrc
identifier DOI: 10.17632/bn7ch8tvyp
ispartof
issn
language eng
recordid cdi_datacite_primary_10_17632_bn7ch8tvyp
source DataCite
title Digital images of defective and good condition tyres
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-21T01%3A39%3A07IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-datacite_PQ8&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=unknown&rft.au=Pathmanaban%20P&rft.date=2023-08-22&rft_id=info:doi/10.17632/bn7ch8tvyp&rft_dat=%3Cdatacite_PQ8%3E10_17632_bn7ch8tvyp%3C/datacite_PQ8%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true