中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
Object detection and recognition system based on computer vision analysis

文献类型:期刊论文

作者Liu,Haitao1; Li,Yuge2; Liu,Dongchang1
刊名Journal of Physics: Conference Series
出版日期2021-07-01
卷号1976期号:1
关键词post-epidemic artificial intelligence computer vision deep learning
ISSN号1742-6588
DOI10.1088/1742-6596/1976/1/012024
英文摘要Abstract Artificial intelligence based on deep learning enables the machine to have the ability of understanding and cognition, but the application of artificial intelligence technology in supermarket shopping scene is limited. In the post-epidemic era, the contactless self-checkout of unmanned supermarket is more in line with the development needs of modern society. We build Pytorch environment, first to collect pictures of a large number of commodities and labeling information, and training model is obtained by YOLO neural network algorithm, finally through a call to model to realize the recognition of goods. Neural network algorithm is used to improve the recognition rate of goods step by step and achieve the detection and recognition of objects. We have tested our model on the real supermarket commodity data set and the public data set ImageNet, and the results show that our model can achieve a certain practical effect.
语种英语
WOS记录号IOP:1742-6588-1976-1-012024
出版者IOP Publishing
源URL[http://ir.ia.ac.cn/handle/173211/45790]  
专题综合信息系统研究中心_脑机融合与认知评估
作者单位1.Institute of Automation, Chinese Academy of Sciences, Beijing, China
2.Renmin University of China, Beijing, China
推荐引用方式
GB/T 7714
Liu,Haitao,Li,Yuge,Liu,Dongchang. Object detection and recognition system based on computer vision analysis[J]. Journal of Physics: Conference Series,2021,1976(1).
APA Liu,Haitao,Li,Yuge,&Liu,Dongchang.(2021).Object detection and recognition system based on computer vision analysis.Journal of Physics: Conference Series,1976(1).
MLA Liu,Haitao,et al."Object detection and recognition system based on computer vision analysis".Journal of Physics: Conference Series 1976.1(2021).

入库方式: OAI收割

来源:自动化研究所

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