Advancing Automation in Aerial Imaging for Marine Litter Detection (MACKIEWICZM_U26CMP)

About the Project

Project supervisor – Professor Michal Mackiewicz

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.

The student 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

The student 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

The student 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. They will undertake training specific to this project including imaging principles, lab measurement, computer vision and ArcGIS, potential fieldwork and UAV flying training.

The School of Computing Sciences provides a vibrant research environment for conducting Computing and allied research and training. We collaborate with multi-national companies such as Apple, BT, the National Trust and Aviva, research institutes in the Norwich Research Park, as well as other universities and industries in the UK and overseas. We are also members of the Turing University Network, a group of 65 UK universities working together to advance world-class research and build skills for the future.

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.

The successful candidate will also be expected to contribute to Tutor activities for laboratory support on our BSc and MSc Courses in Artificial Intelligence, Data Science, Computing Sciences and Cyber Security commensurate with their core expertise, within the working hours permitted for full-time Postgraduate Researchers.

The standard minimum entry requirement is 2:1 in Computer Science or related subject area.

Mode of study

Full-time

Start date

1 October 2026