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LIACi - Lifecycle Inspection, Analysis and Condition information system

Lifecycle Inspection, Analysis and Condition information system

Contact persons

  • Brian Elvesæter

    Brian Elvesæter

    Senior Research Scientist Sustainable Communication Technologies
  • Maryna Waszak

    Maryna Waszak

    Research Scientist Sustainable Communication Technologies

Objective

Develop an adaptive decision support system that improves efficiency of video-based inspections with automatic report generation and extended use of inspection data.

Posicom will use the leading competence of SINTEF and NTNU to extend their existing Seekuence video inspection system with these features:

  1. ML supported tagging of objects and events, and
  2. Contextualization of video with additional information.

This will give Posicom a unique advantage in serving the maritime and fish farming market segments represented by DNV, VUVI, Island Offshore and Mainstay.

Background

Companies across oil & gas, maritime and fish farming value chains are seeking quicker, more accessible, and cost-effective ways to ensure technical safety and performance of projects and operations. Increasingly more of those companies are using digital technologies to virtually bring inspectors and surveyors to sites in order to witness and verify the quality and integrity of equipment and assets to company specifications or industry standards.

Partners

SINTEF´s role in the project

SINTEF is responsible for the technical development of early-stage prototypes.

  • We are developing a video tagging module, applying Machine Learning (ML) to classify, detect and segment interesting findings (e.g., marine growth) in underwater ship hull inspection videos.
  • We are developing a video contextualization module that relates the findings in the inspection videos with additional data in a Knowledge Graph (KG) to support data analytics.

See the demonstrators below for further details.

Demonstrators

Video tagging

ML supported tagging of objects in ship hull inspection videos (e.g., paint peel)

paint_peel_400.png

Video contextualization

contextualization_400.png

Datasets

Underwater ship inspections

  • Website: https://liaci.sintef.cloud
  • First public large-scale semantic segmentation dataset for underwater ship inspections
  • Contains 1893 images with pixel annotations

class_sample_600.png

Funding

LIACi has received funding from The Research Council on Norway under the project No 317854.

Key Factors

Project duration

01/01/2021 - 31/12/2023