O. K. Kolesnytsky, E. V. Yankovsky, I. K. Denisov, I. R. Arsenyuk
{"title":"利用卷积神经网络检测视频流中的武装人员","authors":"O. K. Kolesnytsky, E. V. Yankovsky, I. K. Denisov, I. R. Arsenyuk","doi":"10.31649/1681-7893-2023-46-2-76-83","DOIUrl":null,"url":null,"abstract":"Information technology for detecting armed people is proposed and its software implementation is investigated. The YOLO convolution neural network was used to detect objects in real time. The Python programming language and the PyTorch library were used to develop the neural network. A program designed to detect armed people in a video stream has been created, the functionality of which allows classifying the type of recognized weapon.","PeriodicalId":142101,"journal":{"name":"Optoelectronic information-power technologies","volume":"36 6","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Detection of armed people in a video stream using convolutional neural networks\",\"authors\":\"O. K. Kolesnytsky, E. V. Yankovsky, I. K. Denisov, I. R. Arsenyuk\",\"doi\":\"10.31649/1681-7893-2023-46-2-76-83\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Information technology for detecting armed people is proposed and its software implementation is investigated. The YOLO convolution neural network was used to detect objects in real time. The Python programming language and the PyTorch library were used to develop the neural network. A program designed to detect armed people in a video stream has been created, the functionality of which allows classifying the type of recognized weapon.\",\"PeriodicalId\":142101,\"journal\":{\"name\":\"Optoelectronic information-power technologies\",\"volume\":\"36 6\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-12-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Optoelectronic information-power technologies\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.31649/1681-7893-2023-46-2-76-83\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Optoelectronic information-power technologies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.31649/1681-7893-2023-46-2-76-83","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Detection of armed people in a video stream using convolutional neural networks
Information technology for detecting armed people is proposed and its software implementation is investigated. The YOLO convolution neural network was used to detect objects in real time. The Python programming language and the PyTorch library were used to develop the neural network. A program designed to detect armed people in a video stream has been created, the functionality of which allows classifying the type of recognized weapon.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
