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Optimization of Decision Support Technology for Offshore Oil Condition Monitoring with Carbon Neutrality as the Goal in the Enterprise Development Process

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成果类型:
期刊论文
作者:
Gao, Shiya;Guan, Xin;Cao, Xiaojing;Bai, Zhili;Wang, Caimeng;...
通讯作者:
Yu, HY
作者机构:
[Gao, Shiya] Wuhan Polytech Univ, Sch Management, Wuhan, Peoples R China.
[Guan, Xin] Guangzhou Xinhua Univ, Dongguan, Peoples R China.
[Cao, Xiaojing] London Metropolitan Univ, Business Adm, London, England.
[Bai, Zhili] Univ New South Wales, Sch Elect Engn & Telecommun, Sydney, Australia.
[Wang, Caimeng] Guangzhou Univ, Sch Management, Guangzhou, Peoples R China.
通讯机构:
[Yu, HY ] G
Guangzhou Yi Wu Vocat Training Sch, Guangzhou, Peoples R China.
语种:
英文
期刊:
PLOS ONE
ISSN:
1932-6203
年:
2025
卷:
20
期:
3
页码:
e0319858
基金类别:
National Natural Science Foundation of China on "Health inequality related to air pollution: Level measurement, policy evaluation and mechanism testing" [72274145]
机构署名:
本校为第一机构
院系归属:
管理学院
摘要:
This study aims to explore the integration of the Faster R-CNN (Region-based Convolutional Neural Network) algorithm from deep learning into the MobileNet v2 architecture, within the context of enterprises aiming for carbon neutrality in their development process. The experiment develops a marine oil condition monitoring and classification model based on the fusion of MobileNet v2 and Faster R-CNN algorithms. This model utilizes the MobileNet v2 network to extract rich feature information from input images and combines the Faster R-CNN algorithm to rapidly and accurately generate candidate reg...

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