{"title":"基于BP神经网络的在役管道焊缝图像缺陷识别研究","authors":"Yin Jian, Gao Yuan","doi":"10.1109/EIIS.2017.8298623","DOIUrl":null,"url":null,"abstract":"This paper applied computer aided technique to do the image recognition work of the welding-line's defects. And this paper uses aspect ratio, roundness, compactness, symmetry, steepness, gray contrast of defect and the background, position of the defect as the defects' eigenvalues. And this paper applied BP neural network to recognize the defects. And experiments are used to get the best values of the eigenvalues.","PeriodicalId":434246,"journal":{"name":"2017 First International Conference on Electronics Instrumentation & Information Systems (EIIS)","volume":"93 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Research on the defects identify of the weld-line's image of the in-service pipeline based on BP neural network\",\"authors\":\"Yin Jian, Gao Yuan\",\"doi\":\"10.1109/EIIS.2017.8298623\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper applied computer aided technique to do the image recognition work of the welding-line's defects. And this paper uses aspect ratio, roundness, compactness, symmetry, steepness, gray contrast of defect and the background, position of the defect as the defects' eigenvalues. And this paper applied BP neural network to recognize the defects. And experiments are used to get the best values of the eigenvalues.\",\"PeriodicalId\":434246,\"journal\":{\"name\":\"2017 First International Conference on Electronics Instrumentation & Information Systems (EIIS)\",\"volume\":\"93 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 First International Conference on Electronics Instrumentation & Information Systems (EIIS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/EIIS.2017.8298623\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 First International Conference on Electronics Instrumentation & Information Systems (EIIS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EIIS.2017.8298623","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Research on the defects identify of the weld-line's image of the in-service pipeline based on BP neural network
This paper applied computer aided technique to do the image recognition work of the welding-line's defects. And this paper uses aspect ratio, roundness, compactness, symmetry, steepness, gray contrast of defect and the background, position of the defect as the defects' eigenvalues. And this paper applied BP neural network to recognize the defects. And experiments are used to get the best values of the eigenvalues.