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Real-Time Multi-Class Disturbance Detection for Φ-OTDR Based on YOLO Algorithm.
Xu, Weijie; Yu, Feihong; Liu, Shuaiqi; Xiao, Dongrui; Hu, Jie; Zhao, Fang; Lin, Weihao; Wang, Guoqing; Shen, Xingliang; Wang, Weizhi; Wang, Feng; Liu, Huanhuan; Shum, Perry Ping; Shao, Liyang.
Afiliação
  • Xu W; Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen 518055, China.
  • Yu F; Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen 518055, China.
  • Liu S; Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen 518055, China.
  • Xiao D; Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau 999078, China.
  • Hu J; Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen 518055, China.
  • Zhao F; Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen 518055, China.
  • Lin W; Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen 518055, China.
  • Wang G; Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen 518055, China.
  • Shen X; Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau 999078, China.
  • Wang W; Department of Microelectronics, Shenzhen Institute of Information Technology, Shenzhen 518172, China.
  • Wang F; Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen 518055, China.
  • Liu H; The Department of Electronic and Information Engineering, Hong Kong Polytechnic University, Kowloon, Hong Kong, China.
  • Shum PP; Peng Cheng Laboratory, Shenzhen 518005, China.
  • Shao L; College of Engineering and Applied Sciences, Nanjing University, Nanjing 210023, China.
Sensors (Basel) ; 22(5)2022 Mar 03.
Article em En | MEDLINE | ID: mdl-35271143
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Algoritmos Tipo de estudo: Diagnostic_studies / Prognostic_studies Idioma: En Revista: Sensors (Basel) Ano de publicação: 2022 Tipo de documento: Article País de afiliação: China
Texto completo
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Algoritmos Tipo de estudo: Diagnostic_studies / Prognostic_studies Idioma: En Revista: Sensors (Basel) Ano de publicação: 2022 Tipo de documento: Article País de afiliação: China