The Defect Detection Algorithm for Tire X-Ray Images Based on Deep Learning

Qidan Zhu, X. Ai
{"title":"The Defect Detection Algorithm for Tire X-Ray Images Based on Deep Learning","authors":"Qidan Zhu, X. Ai","doi":"10.1109/ICIVC.2018.8492908","DOIUrl":null,"url":null,"abstract":"For current domestic and international tire detection systems, the software operation of them is complex and poor to be put into application. In reality, it is necessary to do the task of defect detection by observing the X-ray image of the tire with the help of human eyes. This practice is affected by some subjective factors and both the accuracy and efficiency vary from person to person without strong robustness. To tackle this issue, one detection algorithm for tire defects based on deep learning is proposed. In this case, the model is trained, learnt and tested using the collected defect samples preprocessed from tire X-ray images. The designed algorithm was verified by the developed automatic tire defect detection software, in which the desired results were obtained.","PeriodicalId":173981,"journal":{"name":"2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"17","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIVC.2018.8492908","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 17

Abstract

For current domestic and international tire detection systems, the software operation of them is complex and poor to be put into application. In reality, it is necessary to do the task of defect detection by observing the X-ray image of the tire with the help of human eyes. This practice is affected by some subjective factors and both the accuracy and efficiency vary from person to person without strong robustness. To tackle this issue, one detection algorithm for tire defects based on deep learning is proposed. In this case, the model is trained, learnt and tested using the collected defect samples preprocessed from tire X-ray images. The designed algorithm was verified by the developed automatic tire defect detection software, in which the desired results were obtained.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于深度学习的轮胎x射线图像缺陷检测算法
目前国内外的轮胎检测系统软件操作复杂,实用化差。在现实中,需要借助人眼观察轮胎的x射线图像来完成缺陷检测任务。这种做法受到一些主观因素的影响,准确性和效率因人而异,没有很强的稳健性。为了解决这一问题,提出了一种基于深度学习的轮胎缺陷检测算法。在这种情况下,使用从轮胎x射线图像中预处理的收集的缺陷样本对模型进行训练、学习和测试。通过开发的轮胎缺陷自动检测软件对所设计的算法进行了验证,得到了预期的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
An Investigation of Skeleton-Based Optical Flow-Guided Features for 3D Action Recognition Using a Multi-Stream CNN Model Research on the Counting Algorithm of Bundled Steel Bars Based on the Features Matching of Connected Regions Hybrid Change Detection Based on ISFA for High-Resolution Imagery Scene Recognition with Convolutional Residual Features via Deep Forest Design and Implementation of T-Hash Tree in Main Memory Data Base
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1