{"title":"基于深度神经网络的铆钉接头制造缺陷视频图像检测与识别","authors":"O. S. Amosov, S. G. Amosova, I. O. Iochkov","doi":"10.1109/FarEastCon.2019.8934095","DOIUrl":null,"url":null,"abstract":"The issue of detecting and recognizing manufacturing defects in riveted joints using video content is presented. For the detection and classification of defects in riveted joints of aircraft, a computational method using deep neural networks has been developed. An illustrative example is given.","PeriodicalId":395247,"journal":{"name":"2019 International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Detection and Recognition of Manufacturing Defects of Rivet Joints by their Video Images Using Deep Neural Networks\",\"authors\":\"O. S. Amosov, S. G. Amosova, I. O. Iochkov\",\"doi\":\"10.1109/FarEastCon.2019.8934095\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The issue of detecting and recognizing manufacturing defects in riveted joints using video content is presented. For the detection and classification of defects in riveted joints of aircraft, a computational method using deep neural networks has been developed. An illustrative example is given.\",\"PeriodicalId\":395247,\"journal\":{\"name\":\"2019 International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon)\",\"volume\":\"102 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/FarEastCon.2019.8934095\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FarEastCon.2019.8934095","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Detection and Recognition of Manufacturing Defects of Rivet Joints by their Video Images Using Deep Neural Networks
The issue of detecting and recognizing manufacturing defects in riveted joints using video content is presented. For the detection and classification of defects in riveted joints of aircraft, a computational method using deep neural networks has been developed. An illustrative example is given.