Вадим Горбачев, V. Gorbachev, Юрий Блохинов, Y. Blokhinov, Андрей Никитин, A. Nikitin, Екатерина Андриенко, E. Andrienko
{"title":"基于CNN检测器的室内无人机定位技术","authors":"Вадим Горбачев, V. Gorbachev, Юрий Блохинов, Y. Blokhinov, Андрей Никитин, A. Nikitin, Екатерина Андриенко, E. Andrienko","doi":"10.30987/graphicon-2019-2-280-284","DOIUrl":null,"url":null,"abstract":"The article presents the drone positioning technology in a multi-camera system by using the detection algorithm. Paper describes positioning system and algorithm for calculating 3d drone coordinates based on its image position, detected on images of stationary video cameras. Positioning enables automatically control the drone when precise data from satellite navigation systems are not available, for example, in closed hangars. The developed technology is used to create a complex of automatic visual control of aircraft. The ways of adaptation of neural network detection algorithm to the problem of drone detection are presented. The main attention is paid to the methods of training data preparation. It is shown that high accuracy can be achieved using synthesized images without any real data or manual labelling.","PeriodicalId":409819,"journal":{"name":"GraphiCon'2019 Proceedings. Volume 2","volume":"207 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Technology for Indoor Drone Positioning Based on CNN Detector\",\"authors\":\"Вадим Горбачев, V. Gorbachev, Юрий Блохинов, Y. Blokhinov, Андрей Никитин, A. Nikitin, Екатерина Андриенко, E. Andrienko\",\"doi\":\"10.30987/graphicon-2019-2-280-284\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The article presents the drone positioning technology in a multi-camera system by using the detection algorithm. Paper describes positioning system and algorithm for calculating 3d drone coordinates based on its image position, detected on images of stationary video cameras. Positioning enables automatically control the drone when precise data from satellite navigation systems are not available, for example, in closed hangars. The developed technology is used to create a complex of automatic visual control of aircraft. The ways of adaptation of neural network detection algorithm to the problem of drone detection are presented. The main attention is paid to the methods of training data preparation. It is shown that high accuracy can be achieved using synthesized images without any real data or manual labelling.\",\"PeriodicalId\":409819,\"journal\":{\"name\":\"GraphiCon'2019 Proceedings. Volume 2\",\"volume\":\"207 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-11-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"GraphiCon'2019 Proceedings. Volume 2\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.30987/graphicon-2019-2-280-284\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"GraphiCon'2019 Proceedings. Volume 2","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.30987/graphicon-2019-2-280-284","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Technology for Indoor Drone Positioning Based on CNN Detector
The article presents the drone positioning technology in a multi-camera system by using the detection algorithm. Paper describes positioning system and algorithm for calculating 3d drone coordinates based on its image position, detected on images of stationary video cameras. Positioning enables automatically control the drone when precise data from satellite navigation systems are not available, for example, in closed hangars. The developed technology is used to create a complex of automatic visual control of aircraft. The ways of adaptation of neural network detection algorithm to the problem of drone detection are presented. The main attention is paid to the methods of training data preparation. It is shown that high accuracy can be achieved using synthesized images without any real data or manual labelling.