{"title":"Intel OpenVINO计算机视觉工具包:对象检测和语义分割","authors":"V. V. Zunin","doi":"10.1109/RusAutoCon52004.2021.9537452","DOIUrl":null,"url":null,"abstract":"The paper provides an overview of the neural networks implementation current state, their methods of execution, and the Intel® OpenVINO ™ Toolkit for executing neural networks on various hardware platforms from Intel. This work describes the selection of computer vision neural networks and datasets for object detection and semantic segmentation of images for subsequent testing. It gives the description of the experiment on various hardware platforms. Moreover, it provides an analysis of the performance and cost of running selected neural networks using the OpenVINO ™ Toolkit in normal mode, as well as using plug-ins for multiple devices and heterogeneous plug-ins on multiple connected devices.","PeriodicalId":106150,"journal":{"name":"2021 International Russian Automation Conference (RusAutoCon)","volume":"175 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Intel OpenVINO Toolkit for Computer Vision: Object Detection and Semantic Segmentation\",\"authors\":\"V. V. Zunin\",\"doi\":\"10.1109/RusAutoCon52004.2021.9537452\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The paper provides an overview of the neural networks implementation current state, their methods of execution, and the Intel® OpenVINO ™ Toolkit for executing neural networks on various hardware platforms from Intel. This work describes the selection of computer vision neural networks and datasets for object detection and semantic segmentation of images for subsequent testing. It gives the description of the experiment on various hardware platforms. Moreover, it provides an analysis of the performance and cost of running selected neural networks using the OpenVINO ™ Toolkit in normal mode, as well as using plug-ins for multiple devices and heterogeneous plug-ins on multiple connected devices.\",\"PeriodicalId\":106150,\"journal\":{\"name\":\"2021 International Russian Automation Conference (RusAutoCon)\",\"volume\":\"175 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-09-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 International Russian Automation Conference (RusAutoCon)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/RusAutoCon52004.2021.9537452\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 International Russian Automation Conference (RusAutoCon)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/RusAutoCon52004.2021.9537452","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Intel OpenVINO Toolkit for Computer Vision: Object Detection and Semantic Segmentation
The paper provides an overview of the neural networks implementation current state, their methods of execution, and the Intel® OpenVINO ™ Toolkit for executing neural networks on various hardware platforms from Intel. This work describes the selection of computer vision neural networks and datasets for object detection and semantic segmentation of images for subsequent testing. It gives the description of the experiment on various hardware platforms. Moreover, it provides an analysis of the performance and cost of running selected neural networks using the OpenVINO ™ Toolkit in normal mode, as well as using plug-ins for multiple devices and heterogeneous plug-ins on multiple connected devices.