Oscar Bryan

Machine Learning for the Detection of Unexploded Ordnance Using Synthetic Aperture Sonar

Project Summary

My research area is the application of machine learning methods for the detection of sea disposed, unexploded ordnance. Advances in underwater sensing technologies (synthetic aperture Sonar) and autonomous vehicles has resulted in large amounts of survey data at a high (cm) resolution. This development motivates the development of autonomous mapping and assessment for the remediation of historic weapons dumped at sea.


Doctoral Recognition Award Winner 2021

Research Interests

Sonar, deep learning, Bayesian machine learning and explainable AI.


MEng Mechanical Engineering at University of Bath, ART-AI MRes


Dr Alan Hunter

Dr Tom Fincham Haines

Dr Roy Edgar Hansen (Forsvarets Forskningsinstitutt and University of Oslo)

Dr Narada Warakagoda (Forsvarets Forskningsinstitutt and University of Oslo)