article · Journal of Low Power Electronics and Applications · 2023

FTFNet: Multispectral Image Segmentation

Cited by 1Open access

Abstract

Semantic segmentation is a machine learning task that is seeing increased utilization in multiple fields, from medical imagery to land demarcation and autonomous vehicles. A real-time autonomous system must be lightweight while maintaining reasonable accuracy. This research focuses on leveraging the fusion of long-wave infrared (LWIR) imagery with visual spectrum imagery to fill in the inherent performance gaps when using visual imagery alone. This approach culminated in the Fast Thermal Fusion Network (FTFNet), which shows marked improvement over the baseline architecture of the Multispectral Fusion Network (MFNet) while maintaining a low footprint.

Cite this paper

APA 7

Edwards, J., & El‐Sharkawy, M. (2023). FTFNet: Multispectral Image Segmentation. Journal of Low Power Electronics and Applications, 13(3), 42. https://doi.org/10.3390/jlpea13030042

MLA 9

Edwards, Justin, and Mohamed El‐Sharkawy. “FTFNet: Multispectral Image Segmentation.” Journal of Low Power Electronics and Applications, vol. 13, no. 3, 2023, p. 42, https://doi.org/10.3390/jlpea13030042.

Chicago

Edwards, Justin, and Mohamed El‐Sharkawy. 2023. “FTFNet: Multispectral Image Segmentation.” Journal of Low Power Electronics and Applications 13 (3): 42. https://doi.org/10.3390/jlpea13030042.

Harvard

Edwards, J. and El‐Sharkawy, M. (2023) ‘FTFNet: Multispectral Image Segmentation’, Journal of Low Power Electronics and Applications, 13(3), p. 42. Available at: https://doi.org/10.3390/jlpea13030042.

BibTeX

@article{edwards2023ftfnet,
  title = {{FTFNet: Multispectral Image Segmentation}},
  author = {Edwards, Justin and El‐Sharkawy, Mohamed},
  journal = {Journal of Low Power Electronics and Applications},
  volume = {13},
  number = {3},
  pages = {42},
  year = {2023},
  doi = {10.3390/jlpea13030042}
}

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