Ship Deck Segmentation In Engineering Document Using Generative Adversarial Networks

• Published in: 2022 IEEE World AI IoT Congress (AIIoT) pp. 207 - 212
• Main Authors: Uddin, Mohammad Shahab, Pamie-George, Raphael, Wilkins, Daron, Sousa-Poza, Andres, Canan, Mustafa, Kovacic, Samuel, Li, Jiang
• Format: Conference Proceeding
• Language: English
• Published: IEEE 06-06-2022
• Subjects: Data models; GAN; Generative adversarial networks; Image segmentation; Pix2Pix GAN; ship deck segmentation; Superresolution; Three-dimensional displays; Training; Training data
• DOI: 10.1109/AIIoT54504.2022.9817355

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.