{"title":"基于Yolo9000的安全检测对象的检测与识别","authors":"Zhongqiu Liu, Jianchao Li, Y. Shu, Dongping Zhang","doi":"10.1109/ICSAI.2018.8599420","DOIUrl":null,"url":null,"abstract":"In this paper, a convolutional neural network model based on YOLO9000 is introduced to meet the need of real-time engineering computing. This network model can study and classify the targets in depth, aiming at the characteristics of scissors and aerosols. The characteristics have various kinds such as overlap, cover and multiscale. At the present stage, the average speed is 68 FPS on the windows platform with GPU (Geforce GTX Titan X) acceleration. In addition, the average precision and recall rate are 94. 5%, 92. 6%, respectively.","PeriodicalId":375852,"journal":{"name":"2018 5th International Conference on Systems and Informatics (ICSAI)","volume":"306 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"23","resultStr":"{\"title\":\"Detection and Recognition of Security Detection Object Based on Yolo9000\",\"authors\":\"Zhongqiu Liu, Jianchao Li, Y. Shu, Dongping Zhang\",\"doi\":\"10.1109/ICSAI.2018.8599420\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, a convolutional neural network model based on YOLO9000 is introduced to meet the need of real-time engineering computing. This network model can study and classify the targets in depth, aiming at the characteristics of scissors and aerosols. The characteristics have various kinds such as overlap, cover and multiscale. At the present stage, the average speed is 68 FPS on the windows platform with GPU (Geforce GTX Titan X) acceleration. In addition, the average precision and recall rate are 94. 5%, 92. 6%, respectively.\",\"PeriodicalId\":375852,\"journal\":{\"name\":\"2018 5th International Conference on Systems and Informatics (ICSAI)\",\"volume\":\"306 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"23\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 5th International Conference on Systems and Informatics (ICSAI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICSAI.2018.8599420\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 5th International Conference on Systems and Informatics (ICSAI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSAI.2018.8599420","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Detection and Recognition of Security Detection Object Based on Yolo9000
In this paper, a convolutional neural network model based on YOLO9000 is introduced to meet the need of real-time engineering computing. This network model can study and classify the targets in depth, aiming at the characteristics of scissors and aerosols. The characteristics have various kinds such as overlap, cover and multiscale. At the present stage, the average speed is 68 FPS on the windows platform with GPU (Geforce GTX Titan X) acceleration. In addition, the average precision and recall rate are 94. 5%, 92. 6%, respectively.
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