Partnership develops real-time marine mammal detection system

The ��ñ�� and MSeis are working together through a Knowledge Transfer Partnership

Aerial view of pod of bottlenose dolphin swimming in the ocean

Partnership develops real-time marine mammal detection system

The ��ñ�� and MSeis are working together through a Knowledge Transfer Partnership

Mr Alan Williams

Media and Communications Manager

Corporate Communications (Marketing and Communications)

9 March 2026

A new partnership spanning science and industry is working to develop an AI-powered, real-time detection system with the ability to track marine mammals as they move along the UK coastline.

The project is being driven through a Knowledge Transfer Partnership (KTP) that combines expertise in marine conservation, robotics, marine autonomy and sensor technology from the ��ñ�� and MSeis.

Supported by Innovate UK, the project team will integrate advanced computer vision models into MSeis��ñ��ޙs dual-sensor thermal and optical camera platforms to automate the detection and identification of marine mammals and floating anomalies such as debris.

With its development informed by expert spotters, the system will significantly enhance detection accuracy, operational efficiency, and offshore safety.

Ultimately, it aims to transform a traditionally manual and observation-dependent process into a robust, intelligent and scalable solution that sets a new technological benchmark in environmental monitoring and marine risk mitigation.

The project is being conducted at a time when marine industries, particularly offshore renewables and subsea operations, are expanding rapidly with an increasing need for reliable environmental monitoring and marine wildlife protection.

Technological advances now make real-time automated detection feasible, enabling this project to support safer offshore operations while contributing to long-term marine conservation and industry transformation.

This partnership demonstrates how advanced computer vision and artificial intelligence can move beyond the laboratory and directly support marine conservation and offshore safety. By combining multimodal sensing with real-time computer vision, we are creating a robust system capable of operating in complex, real-world environments.

Dr Dena Bazazian
Lecturer in Robotics and Machine Vision

This KTP enables us to embed cutting-edge AI into our technology portfolio, strengthening our capability to deliver smarter, safer, and more sustainable marine monitoring solutions. Working with the ��ñ�� allows us to accelerate innovation and lead industry transformation.

Mark Higginbottom
Managing Director at MSeis

The University team working on the project includes Dr Bazazian, Lecturer in Computer Science Dr Haoyi Wang, Research Assistant in Coastal Processes Dr Aikaterini Konstantinou, and Associate Professor of Marine Conservation Dr Simon Ingram. Between them, they possess expertise in deep learning, real-time computer vision and AI deployment in challenging marine environments, in addition to decades spent monitoring marine mammals ��ñ��ޓ often working alongside University students ��ñ��ޓ off the UK coast.

This expertise will be combined with that of the MSeis team, which has developed advanced dual-sensor camera technology and also possesses offshore operational experience and industry insight into marine mammal monitoring workflows. A KTP Associate, Dr Abel Pacheco Ortega, will play a central role in embedding AI and computer vision capability within MSeis��ñ��ޙ technology, ensuring long-term knowledge transfer and sustainable in-house expertise development.

Together, they will work towards a series of key deliverables including: a fully integrated AI-powered computer vision detection system embedded within MSeis camera platforms; real-time marine mammal and debris detection capability; a continuously trainable computer vision and deep learning model; and deployment-ready software validated in offshore conditions.

The project will strengthen MSeis��ñ��ޙs market position, support regulatory compliance in marine operations, and establish a scalable AI and computer vision framework adaptable to other environmental monitoring researchers.

��ñ��