Computer Vision & Perception
State-of-the-art algorithms for object detection, semantic segmentation, 3D reconstruction, and scene understanding — enabling machines to interpret the visual world.
Computer Vision & Perception
Our computer vision research pushes the boundaries of visual intelligence — from real-time object detection in low-resource environments to full 3D scene reconstruction. We develop transformer-based architectures and lightweight inference pipelines suited to edge deployment in African contexts where compute is constrained.
Key Research Topics
Object Detection
Real-time multi-class detection using YOLO-family and transformer-based detectors, optimised for low-power embedded systems.
Scene Segmentation
Pixel-level semantic and panoptic segmentation for autonomous driving, medical imaging, and satellite scene parsing.
3D Reconstruction
Neural Radiance Fields (NeRF) and photogrammetry pipelines for high-fidelity 3D scene and object reconstruction.
Edge Inference
Quantised, pruned models and TensorRT deployment strategies enabling full vision pipelines on edge and mobile hardware.
Current Projects
EdgeSight: Low-Power Detection Framework
A family of quantised YOLO-variant models and inference runtimes enabling real-time object detection on Raspberry Pi 4 and similar edge hardware for wildlife monitoring and agricultural scouting.…
Active · 2024–2026Scene3D: Neural Radiance Field Reconstruction
NeRF and Gaussian-splatting pipeline for rapid, photorealistic 3D scene reconstruction from smartphone video, with applications in cultural heritage digitisation and remote infrastructure inspection.…
Research · 2025Related Publications
Robust Biometric Authentication Under Signal Noise
Kwame Mensah, Elena Rodriguez
IEEE Transactions on Pattern Analysis and Machine Intelligence
Interested in Collaborating?
We welcome partnerships with institutions, NGOs, and researchers working at the frontier of AI and this domain.