Optimization of Decision Support Technology for Offshore Oil Condition Monitoring with Carbon Neutrality as the Goal in the Enterprise Development Process
Shiya Gao, Xin Guan, Xiaojing Cao, Zhili Bai, Caimeng Wang, Yun Zhan, Haiyang Yu
TL;DR
This paper develops a deep learning model combining MobileNet v2 and Faster R-CNN to improve offshore oil condition monitoring, supporting carbon neutrality goals.
Contribution
A novel hybrid model using MobileNet v2 and Faster R-CNN for oil condition monitoring with high accuracy and low loss.
Findings
The model achieves 90.51% accuracy on the training set and 93.08% on the testing set.
The average loss value of the model is approximately 0.45, indicating strong performance.
The model outperforms other algorithms in oil condition recognition accuracy.
Abstract
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 regions for oil condition monitoring, followed by detailed feature fusion and classification of these regions. The performance of the model is evaluated through experimental assessments. The results demonstrate that the average loss value of the proposed model is approximately 0.45. Moreover, the…
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Taxonomy
TopicsWater Quality Monitoring Technologies · Oil and Gas Production Techniques · Air Quality Monitoring and Forecasting