PhD Studentship: Advancing Automation in Aerial Imaging for Marine Litter Detection

Scientific background 

Marine litter is a key threat to the oceans’ health and the livelihoods that depend on it. Hence, new scalable automated methods to collect and analyse data are needed to enhance our understanding of sources, pathways and impact of litter. Cefas is developing a visible light (VL) deep learning (DL) algorithm and collected a large 89 litter category training dataset. However, there is a recognition of the need of multispectral imagery to enhance the accuracy of the algorithms being developed when discerning material type. Consequently, Cefas is developing a new lab facility to assist in characterisation of multispectral reflectance of materials.  

You will develop existing VL work on reflectance signature of materials extending it to multispectral imaging. The development of robust litter detection and classification algorithms will require an approach that considers the physics of the multispectral image formation including the three key variables: sensor spectral sensitivities, varying daylight illumination spectrum and wide range of relevant material reflectance spectra.  

Research methodology 

You will utilise the existing VL database of key materials, but importantly will also collect multispectral data with the enhanced lab setup with an aim to train the DL algorithms. Importantly, the algorithms developed must be robust to changing real-world illumination and utilised long-term, likely with imaging devices not existing during the development. Therefore, the algorithms are required to have a level of independence to the number of multispectral channels available and their spectral sensitivities. The research will examine several approaches including device independent data representations and/or various transfer learning and domain adaptation techniques. 

Training 

You will be based at the Colour & Imaging Lab at the School of Computing Sciences which has expertise in the design and evaluation of imaging solutions and will have an opportunity to work with scientists and engineers at Cefas. You will undertake training specific to this project including imaging principles, lab measurement, computer vision and ArcGIS, potential fieldwork and UAV flying training. 

Person specification

Experience and/or enthusiastic interest in one or more of the of the following areas interest in environmental monitoring, AI, computer vision or multispectral imaging.  

Entry Requirements

The minimum entry requirement is 2:1 in a Bachelor’s degree in Computer Science/Physics/Maths or other numerate discipline.

Start Date: 1 October 2025

Funding Details

Additional Funding Information

ARIES is awaiting confirmation of funding under the BBSRC-NERC DLA award scheme, which is expected shortly. Funding for this studentship is subject to this confirmation and UKRI terms and conditions. Successful candidates who meet UKRI’s eligibility criteria will be awarded a fully-funded ARIES studentship of fees, maintenance stipend (£19,237 p.a. for 2024/25) and research costs. 

A limited number of ARIES studentships are available to International applicants. Please note however that ARIES funding does not cover additional costs associated with relocation to, and living in, the UK. 

ARIES is committed to equality, diversity, widening participation and inclusion in all areas of its operation. We encourage applications from all sections of the community regardless of gender, ethnicity, disability, age, sexual orientation, and transgender status. Projects have been developed with consideration of a safe, inclusive, and appropriate research and fieldwork environment. Academic qualifications are considered alongside non-academic experience, with equal weighting given to experience and potential. 

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