Computer Vision for Marine Ecology
Leveraging AI and emerging imaging technologies to decode biodiversity stability in complex marine environments. Part of the EU Horizon BioEcoOcean project, funded by the University of Pisa.
I am a PhD candidate at the University of Pisa's Department of Biology, working at the intersection of marine ecology and machine intelligence. My research centres on applying AI and emerging imaging technologies, particularly computer vision and deep learning to evaluate biodiversity stability in complex marine environments.
My work spans the full pipeline: from collecting and annotating underwater imagery, training object detection and classification models, to integrating outputs with ecological data for biodiversity assessment. I believe scalable, automated vision systems are key to monitoring ocean health at the speed the climate crisis demands.
I hold an MSc in Coastal & Marine Biology and Ecology from Università del Salento (Unisalento4Talents Scholar) and a BSc in Fisheries and Aquaculture from Federal University Oye-Ekiti, Nigeria, graduating as best student of my department. My PhD is funded by the BioEcoOcean EU Horizon project.
This demo shows a real-time YOLO-based object detection and tracking pipeline applied to underwater video footage. Each species is automatically identified, labelled, and assigned a persistent tracking ID across frames.
Part of ongoing work under BioBoost+ EU project contributing to scalable, AI-driven biodiversity monitoring by eliminating the need for manual video annotation.
From Nigeria to Italy — building the interdisciplinary foundation for AI-driven marine science.
Peer-reviewed articles and conference contributions, newest first.
Tools, pipelines, and experiments in computer vision and marine data science.
A cross-disciplinary stack spanning AI engineering and field marine science.
Research milestones, project updates, and field dispatches.
Available for academic collaborations, consulting, speaking, and research inquiries, especially at the intersection of computer vision, marine science, and biodiversity monitoring.