PhD Studentship: Monitoring Coastal Environments using Imaging Sonars and Machine Learning (MACKIEWICZM_UCMP18NEX)

University of East Anglia - School of Computing Sciences

Start Date: October 2018

Supervisor: Dr Michal Mackiewicz

Project description: Imaging sonars are capable of producing video-like images at frame rates (typically 8-30 f/ps) in the underwater marine environment. Such systems work well in the turbid coastal and estuarine environments. As such imaging sonars provide new remote sensing tools for studying previously intractable problems important to industry and marine managers including detection of potential clogging organisms for power station water intakes and fish behaviour around coastal structures.

The amount of data that such systems can generate (Tb/day) creates a real barrier to  routine deployment. There have been recent advances in the capability of machine vision modules making them now practical components of underwater remote sensing systems. This project aims to develop automated machine learning to detect and classify targets of interest in near real time thereby dramatically reducing the image analysis costs, opening up the use of such systems in autonomous remote sensing applications.


Traditional image processing techniques employed to detect and classify imaging sonar features use pixel-based supervised classification. These are ineffective in scenarios where large quantities of data are available, and the sonar footage may contain few occurrences of relevant objects. The research will concentrate on developing machine learning algorithms capable of aiding processing of the sonar images by letting them learn other imaging domains. The developed algorithms will belong to the family of ‘deep learning’ algorithms, a complex machine learning technique that has recently proven to provide a step-change in a number of computer vision applications. This will require a large dataset of annotated imagery for training and the expert knowledge on the image appearance which are available in Cefas. The student will also contribute to the deployment of Cefas sonars as the research progresses.


The student will be registered at UEA, hosted at School of Computing Sciences in the Graphics, Vision and Speech laboratory and will receive training including computer vision, machine learning as well as Matlab and Python programming. The student will spend periods of time at Cefas, Lowestoft and University of Southampton in order to familiarize with the images and the ecological aspects of the project.

Person specification: Any numerate discipline. 2:1 or equivalent.

Funding notes: Successful candidates who meet RCUK’s eligibility criteria will be awarded a NERC/EPSRC studentship - in 2017/18, the stipend is £14,553. In most cases, UK and EU nationals who have been resident in the UK for 3 years are eligible for a stipend. For non-UK EU-resident applicants NERC funding can be used to cover fees, RTSG and training costs, but not any part of the stipend. Individual institutes may, however, elect to provide a stipend from their own resources. 

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