{"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}
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.
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