{"title":"基于深度学习的车间人员快速检测方法","authors":"Pei Zhang","doi":"10.1109/ICCSMT54525.2021.00054","DOIUrl":null,"url":null,"abstract":"As the basic unit of production activities in the workshop, the staffs have subjective initiative and high degree of uncertainty. Thus, the management of human in workshop is important and difficult. Based on the urgent necessary of real production, an efficient method for detecting workshop staff was proposed. Compared with the existing 3-Stage CCNN and Adaptive Rec-network, the experiments proved that the proposed method has higher accuracy.","PeriodicalId":304337,"journal":{"name":"2021 2nd International Conference on Computer Science and Management Technology (ICCSMT)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Rapid detection method of workshop staff based on deep learning\",\"authors\":\"Pei Zhang\",\"doi\":\"10.1109/ICCSMT54525.2021.00054\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"As the basic unit of production activities in the workshop, the staffs have subjective initiative and high degree of uncertainty. Thus, the management of human in workshop is important and difficult. Based on the urgent necessary of real production, an efficient method for detecting workshop staff was proposed. Compared with the existing 3-Stage CCNN and Adaptive Rec-network, the experiments proved that the proposed method has higher accuracy.\",\"PeriodicalId\":304337,\"journal\":{\"name\":\"2021 2nd International Conference on Computer Science and Management Technology (ICCSMT)\",\"volume\":\"14 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 2nd International Conference on Computer Science and Management Technology (ICCSMT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCSMT54525.2021.00054\",\"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 2nd International Conference on Computer Science and Management Technology (ICCSMT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCSMT54525.2021.00054","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Rapid detection method of workshop staff based on deep learning
As the basic unit of production activities in the workshop, the staffs have subjective initiative and high degree of uncertainty. Thus, the management of human in workshop is important and difficult. Based on the urgent necessary of real production, an efficient method for detecting workshop staff was proposed. Compared with the existing 3-Stage CCNN and Adaptive Rec-network, the experiments proved that the proposed method has higher accuracy.