Advanced Computer Vision-Based Subsea Gas Leaks Monitoring: A Comparison of Two Approaches.

Zhu, Hongwei; Xie, Weikang; Li, Junjie; Shi, Jihao; Fu, Mingfu; Qian, Xiaoyuan; Zhang, He; Wang, Kaikai; Chen, Guoming

Zhu, Hongwei; Xie, Weikang; Li, Junjie; Shi, Jihao; Fu, Mingfu; Qian, Xiaoyuan; Zhang, He; Wang, Kaikai; Chen, Guoming.

Afiliação

Zhu H; Centre for Offshore Engineering and Safety Technology, China University of Petroleum, Qingdao 266580, China.
Xie W; Centre for Offshore Engineering and Safety Technology, China University of Petroleum, Qingdao 266580, China.
Li J; Centre for Offshore Engineering and Safety Technology, China University of Petroleum, Qingdao 266580, China.
Shi J; Centre for Offshore Engineering and Safety Technology, China University of Petroleum, Qingdao 266580, China.
Fu M; Department of Building Environment and Energy Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China.
Qian X; PipeChina West Pipeline Company, Urumqi 830000, China.
Zhang H; School of Emergency Management and Safety Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China.
Wang K; Centre for Offshore Engineering and Safety Technology, China University of Petroleum, Qingdao 266580, China.
Chen G; Centre for Offshore Engineering and Safety Technology, China University of Petroleum, Qingdao 266580, China.

Sensors (Basel) ; 23(5)2023 Feb 25.

Article em En | MEDLINE | ID: mdl-36904768

Palavras-chave

YOLOv4; advanced computer vision; faster R-CNN; optical camera detection; subsea gas leak monitoring

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Sensors (Basel) Ano de publicação: 2023 Tipo de documento: Article País de afiliação: China

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