人工智能框架下的x射线焊缝缺陷识别

Xiao-xing Feng, Weixin Gao, Zheng Wang, Xiao-meng Wu
{"title":"人工智能框架下的x射线焊缝缺陷识别","authors":"Xiao-xing Feng, Weixin Gao, Zheng Wang, Xiao-meng Wu","doi":"10.1109/ICMCCE51767.2020.00261","DOIUrl":null,"url":null,"abstract":"In view of the need of automatic detection of weld defects, an automatic extraction and classification algorithm for welding defect features based on convolution neural network is proposed. The algorithm directly takes the preprocessed weld images as the input and the welding defect type as the output, effectively avoiding the adverse effect of artificial identification subjective experience on the detection results. The experimental results show that the welding defect identification technology based on convolution neural network has a good identification rate and can provide an important reference for the research of welding quality detection.","PeriodicalId":6712,"journal":{"name":"2020 5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE)","volume":"1 1","pages":"1186-1189"},"PeriodicalIF":0.0000,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Identification of X-ray Weld Defects under Artificial Intelligence Framework\",\"authors\":\"Xiao-xing Feng, Weixin Gao, Zheng Wang, Xiao-meng Wu\",\"doi\":\"10.1109/ICMCCE51767.2020.00261\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In view of the need of automatic detection of weld defects, an automatic extraction and classification algorithm for welding defect features based on convolution neural network is proposed. The algorithm directly takes the preprocessed weld images as the input and the welding defect type as the output, effectively avoiding the adverse effect of artificial identification subjective experience on the detection results. The experimental results show that the welding defect identification technology based on convolution neural network has a good identification rate and can provide an important reference for the research of welding quality detection.\",\"PeriodicalId\":6712,\"journal\":{\"name\":\"2020 5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE)\",\"volume\":\"1 1\",\"pages\":\"1186-1189\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICMCCE51767.2020.00261\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMCCE51767.2020.00261","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

针对焊接缺陷自动检测的需要,提出了一种基于卷积神经网络的焊接缺陷特征自动提取与分类算法。该算法直接将预处理后的焊缝图像作为输入,将焊接缺陷类型作为输出,有效避免了人为识别主观经验对检测结果的不利影响。实验结果表明,基于卷积神经网络的焊接缺陷识别技术具有良好的识别率,可为焊接质量检测的研究提供重要参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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
Identification of X-ray Weld Defects under Artificial Intelligence Framework
In view of the need of automatic detection of weld defects, an automatic extraction and classification algorithm for welding defect features based on convolution neural network is proposed. The algorithm directly takes the preprocessed weld images as the input and the welding defect type as the output, effectively avoiding the adverse effect of artificial identification subjective experience on the detection results. The experimental results show that the welding defect identification technology based on convolution neural network has a good identification rate and can provide an important reference for the research of welding quality detection.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Simulation Analysis of Trajectory Control of Tire Bursting Vehicles Based on MPC Research on the Influence of Computer Application on Regional Economic Development Research on Intelligent Analysis Technology of Power Monitoring Video Data Based on Convolutional Neural Network Transmit digital multi-beam forming based on hyperbolic fractional delay filter An Improved Image Entropy Algorithm Suitable for Digital Painting Style
×
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