Document Type
Article
Publication Date
2021
DOI
10.1109/AIIoT54504.2022.9817355
Publication Title
2022 IEEE World AI IoT Congress (AIIoT)
Pages
207-212
Conference Name
2022 IEEE World AI IoT Congress (AIIoT), 06-09 June 2022, Seattle, Wa, USA
Abstract
Generative adversarial networks (GANs) have become very popular in recent years. GANs have proved to be successful in different computer vision tasks including image-translation, image super-resolution etc. In this paper, we have used GAN models for ship deck segmentation. We have used 2D scanned raster images of ship decks provided by US Navy Military Sealift Command (MSC) to extract necessary information including ship walls, objects etc. Our segmentation results will be helpful to get vector and 3D image of a ship that can be later used for maintenance of the ship. We applied the trained models to engineering documents provided by MSC and obtained very promising results, demonstrating that GANs can be potentially good candidates for this research area.
ORCID
0000-0003-0144-9099 (Sousa-Poza)
Original Publication Citation
Uddin, M. S. U., Pamie-George, R., Wilkins, D., Sousa-Poza, A., Canan, M., Kovacic, S., & Li, J. (2022). In 2022 IEEE World AI IoT Congress (AIIoT) (pp. 207-212). IEEE. http://dx.doi.org/10.1109/AIIoT54504.2022.9817355
Repository Citation
Shahab Uddin, Mohammad; Pamie-George, Raphael; Wilkins, Daron; Poza, Andres Sousa; Canan, Mustafa; Kovacic, Samuel; and Li, Jiang, "Ship Deck Segmentation in Engineering Document Using Generative Adversarial Networks" (2021). Engineering Management & Systems Engineering Faculty Publications. 136.
https://digitalcommons.odu.edu/emse_fac_pubs/136
Included in
Artificial Intelligence and Robotics Commons, Data Storage Systems Commons, Military Vehicles Commons
Comments
This material is based upon work supported, in whole or in part, by the U.S. Department of Defense through SERC WRT-1045 under contract HQ0034-13-D-0004.
U.S. Government work not protected by U.S. copyright.