DurTOMD: A Trail-based Off-road Multimodal Dataset for Traversable Pathway Segmentation under Challenging Illumination Conditions (Y. Sun, L. Li, W. E, A. Atapour-Abarghouei, T.P. Breckon). Presented at International Joint Conference on Neural Networks (IJCNN) , IEEE, Rome, Italy, 2025. [pdf] [code] [bib]
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Pathfinder Sensorbees (bio-hybrid environmental sensing), EPSRC IAA 360° video anomaly detection, DSTL persistent wide-area surveillance and an Innovate UK/Evergreen Health Solutions KTP on real-world dermatology AI. He is the Project Lead for the MoniRail KTP which embeds Durham-led AI into MoniRail’s in-service monitoring to predict track and vehicle degradation, optimise condition-based maintenance and enable robust positioning in GNSS-denied settings.
Methodologically, his group advances efficiency of learning, applications of computer vision and deep learning in robotics, robustness under distribution shift (domain adaptation, multi-task learning, label-efficient training), bias-aware medical AI, and perception in difficult sensing regimes (thermal/infrared, panoramic/360°), with open field-ready pipelines. His work also focuses on semi-supervised anomaly/rare-event detection with his GANomaly/Skip-G
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