I joined the CDT in 2017 after graduating from Newcastle University with an MComp in Computer Science with a Year Abroad. Outside of my studies I am the Social Secretary for the University Maths Society.
PhD title
Photo Identification of Marine Cetaceans Using Convolutional Neural Networks
Modelling cetacean (whale, dolphin, and porpoise) population dynamics and behaviour is paramount to effective population management and conservation. Robust data is required for the design and implementation of conservation strategies and to assess the risks presented by anthropogenic activity such as offshore wind turbines and commercial fishing. Moreover, cetaceans make prime candidates for modelling ecosystem change under the ecosystem sentinel concept as they reflect the current state of the ecosystem and respond to change across different spatial and temporal scale.
As the global climate changes and urbanisation of coastal areas intensifies, it is imperative to develop methodologies for quick and effective assessment of the biological and ecological impact of rising sea temperatures, pollution, and habitat degradation. This can be achieved through modelling the population, behaviour, and health of large marine species such as dolphins.
Methodologies of cetacean research includes photo identification (photo-id). Photo-id involves collecting photographic data and identifying individuals based on unique permanent markings, and has been used for more than 40 years for modelling cetacean population dynamics and ecology. Current identification techniques for cetaceans rely heavily on experts manually identifying individuals. This can often be costly due to the number of person-hours required for identification, as well as the large potential for error due to issues such as observer fatigue. Further, individual identification of dolphins within a species is time consuming due to the nature of the task. With progressively more data being collected during fieldwork through increased use of technology, there is an urgent need for an automatic system for quick identification with reduced error rates.
This project addresses these limitations by applying the methodologies, techniques, and computational power of deep learning to the field of marine biology by bringing together a multidisciplinary team from the School of Engineering, the School of Computing, and the School of Natural and Environmental Science’s Marine MEGAfauna Lab.
Deep learning models, specifically Convolutional Neural Networks (CNNs), are trained on high-end computer clusters using the Microsoft Azure Cloud. Once trained, the models can be ran on field deployable computers to perform image analysis in real time from multiple data sources (underwater and above water images, and aerial drone footage). Methodologies incorporating these models will be designed to quickly identify individuals, assess health, analyse behaviour and incorporate remote sensing techniques.
Unlike traditional Computing Science PhDs, my research topic necessitates biological field work. It helps correlate sensor readings with marine life behaviour. This would be hard to achieve without the support of the CDT.
