SegX-Net: A novel image segmentation approach for contrail detection using deep learning
Escalfament global
Science
Global warming
Q
Canvis climàtics -- Mitigació
R
02 engineering and technology
Imatges -- Segmentació
Climate change mitigation
Imaging segmentation
0202 electrical engineering, electronic engineering, information engineering
Medicine
Research Article
DOI:
10.1371/journal.pone.0298160
Publication Date:
2024-03-05T18:24:27Z
AUTHORS (6)
ABSTRACT
Contrails are line-shaped clouds formed in the exhaust of aircraft engines that significantly contribute to global warming. This paper confidently proposes integrating advanced image segmentation techniques to identify and monitor aircraft contrails to address the challenges associated with climate change. We propose the SegX-Net architecture, a highly efficient and lightweight model that combines the DeepLabV3+, upgraded, and ResNet-101 architectures to achieve superior segmentation accuracy. We evaluated the performance of our model on a comprehensive dataset from Google research and rigorously measured its efficacy with metrics such as IoU, F1 score, Sensitivity and Dice Coefficient. Our results demonstrate that our enhancements have significantly improved the efficacy of the SegX-Net model, with an outstanding IoU score of 98.86% and an impressive F1 score of 99.47%. These results unequivocally demonstrate the potential of image segmentation methods to effectively address and mitigate the impact of air conflict on global warming. Using our proposed SegX-Net architecture, stakeholders in the aviation industry can confidently monitor and mitigate the impact of aircraft shrinkage on the environment, significantly contributing to the global fight against climate change.
SUPPLEMENTAL MATERIAL
Coming soon ....
REFERENCES (48)
CITATIONS (6)
EXTERNAL LINKS
PlumX Metrics
RECOMMENDATIONS
FAIR ASSESSMENT
Coming soon ....
JUPYTER LAB
Coming soon ....