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A Cloud Edge Collaborative Intelligence Method of Insulator String Defect Detection for Power IIoT
Song CH(宋纯贺)1,2; Xu WX(徐文想)1; Han, Guangjie3,4; Zeng P(曾鹏)1,2; Wang ZF(王忠锋)1,2; Yu SM(于诗矛)1,2
Department工业控制网络与系统研究室
Source PublicationIEEE Internet of Things Journal
ISSN2327-4662
2020
Volume2020Pages:1-11
Indexed ByEI
EI Accession number20204909563420
Contribution Rank1
Funding OrganizationNational Key R and D Program of China under Grant 2018YFB1700200 ; National Nature Science Foundation of China under Grants U1908212 and 61773368 ; Industrial Internet innovation development project ”edge computing test bed” ; project of Shenzhen science and technology innovation committee No. JCYJ20190809145407809 ; Project of Fujian University of Technology, No. GY-Z19066 ; revitalizing Liaoning Outstanding Talents Project (No. XLYC1907057) ; State Grid Corporation Science and Technology Project (SG2NK00DWJS1800123)
KeywordIIoT artificial intelligence edge computing insulator string defect recognition
Abstract

Using unmanned aerial vehicles (UAVs) for equipment condition monitoring is an important application of industrial Internet of things (IIoT), and the limited energy is the key factor to restrict the application of UAV. In order to reduce the computational load for intelligence computing of UAV, this paper proposes a cloud edge collaborative intelligent method for object detection, and applies it to insulator string recognition defect detection in the power IIoT. First, the impact of the extremely large aspect ratio of object on the detection accuracy and the computational load are analyzed, then the cloud edge collaborative intelligent method for insulator string detection and defect recognition is presented, in which on the UAV side a low cost method is proposed for estimating possible directions of insulator strings, and on the cloud side an effective method is proposed for insulator string defect detection. The experimental results show the effectiveness of the proposed algorithm. To the best knowledge of us, this paper is the first work to analyze the impact of the extremely large aspect ratio of insulator string on the detection accuracy and the computational load.

Language英语
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/28004
Collection工业控制网络与系统研究室
Corresponding AuthorZeng P(曾鹏)
Affiliation1.Key Laboratory of Networked Control Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China
2.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China
3.Fujian Key Lab for Automotive Electronics and Electric Drive, Fujian University of Technology, 350118, China
4.Department of Information and Communication Systems, Hohai University, Changzhou, China
Recommended Citation
GB/T 7714
Song CH,Xu WX,Han, Guangjie,et al. A Cloud Edge Collaborative Intelligence Method of Insulator String Defect Detection for Power IIoT[J]. IEEE Internet of Things Journal,2020,2020:1-11.
APA Song CH,Xu WX,Han, Guangjie,Zeng P,Wang ZF,&Yu SM.(2020).A Cloud Edge Collaborative Intelligence Method of Insulator String Defect Detection for Power IIoT.IEEE Internet of Things Journal,2020,1-11.
MLA Song CH,et al."A Cloud Edge Collaborative Intelligence Method of Insulator String Defect Detection for Power IIoT".IEEE Internet of Things Journal 2020(2020):1-11.
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