Ship Deck Segmentation in Engineering Document Using Generative Adversarial Networks

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

Challenges for the Engineering Drawing Lehigh Steel Collection

International audienceThe Lehigh Steel Collection (LSC) is an extremely large, heterogeneous set of documents dating from the 1960's through the 1990's. It was retrieved by Lehigh University after it acquired research facilities from Bethlehem Steel, a now-bankrupt company that was once the second-largest steel producer and the largest shipbuilder in the United States. The documents account for and describe research and development activities that were conducted on site, and consist of a very wide range of technical documentation, handwritten notes and memos, annotated printed documents, etc. This paper addresses only a sub-part of this collection: the approximately 4000 engineering drawings and blueprints that were retrieved. The challenge resides essentially in the fact that these documents come in different sizes and shapes, in a wide variety of conservation and degradation stages, and more importantly in bulk, and without ground-truth. Making them available to the research community through digitization is one step the good direction, the question now is what to do with them. This paper tries to lay down some first basic stepping stones for enhancing the documents' meta-data and annotations

GETTING A HANDLE ON FLOOR PLAN ANALYSIS - DOOR CLASSIFICATION IN FLOOR PLANS AND A SURVEY ON EXISTING DATASETS

Floor plan interpretation and reconstruction is crucial to enable the transformation of drawings to 3D models or different digital formats. It has recently taken advantage of neural-based architectures, especially in the semantic segmentation field. These techniques perform better than traditional methods, but the results depend mainly on the data used to train the networks, which is often crafted for the specific task being performed, making it hard to reuse for different purposes. In this paper, we conduct a literature survey on the existing datasets for floor plan analysis, and we explore how information regarding door placement and orientation can be recovered without having to change the initial data or model. We propose a two-step recognition method based on image segmentation followed by classification of cropped zones to allow data augmentation during training. In the process, we generate a dataset consisting of 35000 annotated door images extracted from an existing dataset