RFP QuestBeta
Awarded · ResultStage · award

TRINITY HOUSE

STA1488 - University of Essex - Representation of GLA AtoN in AI Systems

ValueValue not published
Awarded
Published3 Jul 2025
RegionNationwide
Outcome — awarded
University of Essex

This is a contract result notice, not an open opportunity. Details from the official award data.

Who to contact
PROCUREMENT SPECIALIST
etender@trinityhouse.co.uk
01255245000

The procurement contact named on the official notice.

The brief

As the adoption of the Maritime Autonomous Surface Ships (MASS) technology continues to grow, it is crucial to ensure the seamless interaction between MASS and the existing maritime infrastructure.

Therefore, it is essential to ascertain whether machine vision systems, integral to the autonomy of MASS, perceive AtoN accurately enough to guarantee safe and efficient operations.

GRAD in collaboration with the University of Essex (UoE), recently completed a research project, which demonstrated the feasibility of using machine vision systems for detecting and classifying AtoNs, with high accuracy.

To view this notice, please click here: https://trinityhouse.delta-esourcing.com/delta/viewNotice.html?noticeId=962252395

Key requirements

What the supplier must deliver

01

As the adoption of the Maritime Autonomous

As the adoption of the Maritime Autonomous Surface Ships (MASS) technology continues to grow, it is crucial to ensure the seamless interaction between MASS and the existing maritime infrastructure.

02

GRAD in collaboration with the University

GRAD in collaboration with the University of Essex (UoE), recently completed a research project, which demonstrated the feasibility of using machine vision systems for detecting and classifying AtoNs, with high accuracy.

Derived from the notice text — always confirm against the original documents.

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Source & provenance
OCID
ocds-h6vhtk-055831
Stage
award · Awarded
Source
Find a Tender
Buyer ref
037063-2025
View the original notice on Find a Tender

Contains public sector information licensed under the Open Government Licence v3.0. Source data © Crown copyright.

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