PhD Studentship: Embedded AI-Powered Marine Biodiversity Monitoring

This PhD project focuses on advancing computer vision and edge-AI technology for real-time marine monitoring. In collaboration with CEFAS (the Centre for Environment, Fisheries, and Aquaculture Science), a global leader in marine science, the project will develop scalable, low-cost embedded vision systems to analyze marine biodiversity and detect anthropogenic debris. The core challenge is creating robust, low cost, and real-time edge-AI algorithms capable of accurately classifying diverse marine species and debris under complex and dynamic underwater conditions.

The demand for such a low-cost system stems from the need to increase efficiency in marine monitoring. Furthermore, existing computer vision solutions often depend on cloud computing infrastructure and require specialized expertise. A scalable, embedded computer vision system that can analyze imagery in real time offers substantial value by enabling more accurate assessments of nearshore vegetation, shellfish stocks, anthropogenic debris, and epibenthic biodiversity.

The project will build on a working prototype, the Neural Network Enhanced Marine Observation system, a low-cost, shallow-water, edge-AI-enabled spatial camera system designed to meet multiple needs in marine biodiversity monitoring. The project aims to develop embedded novel deep learning and computer vision algorithms to extend the system’s capabilities to classify benthic anthropogenic debris and monitor marine biodiversity.

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