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SegX-Net: A novel image segmentation approach for contrail detection using deep learning.
- Source :
-
PloS one [PLoS One] 2024 Mar 05; Vol. 19 (3), pp. e0298160. Date of Electronic Publication: 2024 Mar 05 (Print Publication: 2024). - Publication Year :
- 2024
-
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.<br />Competing Interests: NO authors have competing interests.<br /> (Copyright: © 2024 Nobel et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
- Subjects :
- Aircraft
Benchmarking
Climate Change
Deep Learning
Aviation
Subjects
Details
- Language :
- English
- ISSN :
- 1932-6203
- Volume :
- 19
- Issue :
- 3
- Database :
- MEDLINE
- Journal :
- PloS one
- Publication Type :
- Academic Journal
- Accession number :
- 38442105
- Full Text :
- https://doi.org/10.1371/journal.pone.0298160