Global fisheries supply 80 million tonnes of food for humans each year. Understanding the amount of catch removed from the sea, including the part of the catch that is returned alive or dead, is critical to informing good fisheries management and science. While it is a simple task to know how much catch is landed by a vessel, the amount of bycatch caught is often unknown and has to be estimated. This PhD is focused on using computer vision and AI to automate the task of collecting and processing data on bycatch composition in fisheries at sea using imaging techniques. To date, information on bycatch is usually recorded by observers on board vessels, a process that is time consuming, expensive, and achieves minimal coverage in space and time. Automating the data gathering process would create a step change in our ability to collect data. The project will provide data into the fishery improvement project ‘Project UK’.
The objective of the PhD is to develop the necessary techniques to process and analyse images obtained from an HD camera and a 3D laser scanner. The challenge is to use machine learning to train a model to recognize the catch (scallops) and the bycatch (other species) and to differentiate these images from inert material (e.g. rocks). The reason for using the two approaches (HD camera and laser) is to compare results derived from each or combine the two sources of data in a multimodal system. The lead supervisor has worked previously with Bangor University and Aberystwyth University to make advances on the camera imaging aspect of the project. This is now at a mature stage with new hardware (i.e. a camera with on-board computing capability). The current state of workstream development means that algorithms have been developed such that video images can be sliced into still frame images of valid records ready for species and size determination. The laser aspect of the project is at a concept phase, but the technique is currently applied at Ulster University to scan seabed morphology and habitats, and hence it is known that the application would work in the proposed context.
The multidisciplinary nature of the project requires a large supervisory team. It is expected that the student will be based primarily at Heriot-Watt University but you will be expected to spend several months working with the teams at Aberystwyth University and Ulster University. This is an exciting project at the cutting edge of the use of technology to improve the sustainability of fisheries, with exceptional prospects for employment in academia, Government agencies, commercial fisheries, food processing, defense and other tech sectors.
To apply you must complete our online application form. Please select PhD programme Marine Biology and include the full project title, reference number and supervisor (Prof MJ Kaiser) on your application form. Ensure that all fields marked as ‘required’ are complete.
You must complete the section marked project proposal; upload a supporting statement documenting your reasons for applying to this particular PhD project, and why you are an ideal candidate for the position. You will also need to provide a CV, a copy of your degree certificate/s and relevant transcripts. You will be asked to enter details of an academic referee who will be able to provide a technical reference. Until your nominated referee has uploaded their statement, your application will not be marked as complete and will not be considered by the review panel.
Please contact Prof Michel Kaiser ([Email Address Removed]) for further information or an informal discussion.
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