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:
- ML supported tagging of objects and events, and
- 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.
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.
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.
ML supported tagging of objects in ship hull inspection videos (e.g., paint peel)
- Website: https://liaci-context.sintef.cloud/
- Contextualization demonstrator showing image findings from different ship inspections.
- The vessel names have been anonymized.
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
A list of publications can be found in CRISTIN.
LIACi has received funding from The Research Council on Norway under the project No 317854.