基于小波变换和神经网络的飞机铆接缺陷自动识别方法

O. S. Amosov, S. G. Amosova
{"title":"基于小波变换和神经网络的飞机铆接缺陷自动识别方法","authors":"O. S. Amosov, S. G. Amosova","doi":"10.1109/dspa53304.2022.9790755","DOIUrl":null,"url":null,"abstract":"This paper presents the method for automatic recognition of defects in aircraft riveted joints. The proposed method consists of three steps: pre-processing, feature extraction and classification. Feature extraction was then performed using discrete wavelet transform. Classification was performed using deep neural network. An example of solving the problem of detecting and recognizing a defect in rivets is considered.","PeriodicalId":428492,"journal":{"name":"2022 24th International Conference on Digital Signal Processing and its Applications (DSPA)","volume":"117 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Method for Automatic Recognition of Defects in Aircraft Riveted Joints Using Wavelet Transform and Neural Network\",\"authors\":\"O. S. Amosov, S. G. Amosova\",\"doi\":\"10.1109/dspa53304.2022.9790755\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents the method for automatic recognition of defects in aircraft riveted joints. The proposed method consists of three steps: pre-processing, feature extraction and classification. Feature extraction was then performed using discrete wavelet transform. Classification was performed using deep neural network. An example of solving the problem of detecting and recognizing a defect in rivets is considered.\",\"PeriodicalId\":428492,\"journal\":{\"name\":\"2022 24th International Conference on Digital Signal Processing and its Applications (DSPA)\",\"volume\":\"117 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-03-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 24th International Conference on Digital Signal Processing and its Applications (DSPA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/dspa53304.2022.9790755\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 24th International Conference on Digital Signal Processing and its Applications (DSPA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/dspa53304.2022.9790755","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

提出了一种飞机铆接接头缺陷的自动识别方法。该方法包括预处理、特征提取和分类三个步骤。然后使用离散小波变换进行特征提取。采用深度神经网络进行分类。给出了一个解决铆钉缺陷检测与识别问题的实例。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Method for Automatic Recognition of Defects in Aircraft Riveted Joints Using Wavelet Transform and Neural Network
This paper presents the method for automatic recognition of defects in aircraft riveted joints. The proposed method consists of three steps: pre-processing, feature extraction and classification. Feature extraction was then performed using discrete wavelet transform. Classification was performed using deep neural network. An example of solving the problem of detecting and recognizing a defect in rivets is considered.
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
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信