Research
Computer Vision
Object detection and tracking, video understanding, vision-language models, document intelligence and OCR, real-time inference on edge devices.
What we work on
- 01Object detection, classification, segmentation and multi-object tracking
- 02Video understanding and temporally consistent analysis
- 03Vision-language models (VLM), document intelligence and OCR
- 04Edge deployment: distillation, quantisation, TensorRT acceleration, and measuring the latency–accuracy trade-off
- 05Data capture through non-standard imaging methods (alternative light sources, spectral imaging)
Why we work in this area
In vision models the gap between laboratory results and field results is larger than in any other area. Lighting changes, camera angles drift, line speed increases, hardware is limited. A model performing well on a test set does not mean it will run for hours on a production line without failing.
That is why the weight of our work sits on real-time behaviour and robustness: keeping a model's accuracy while running it on constrained hardware, uninterrupted and with predictable latency.
From our research projects
Our funded projects in this area concentrate on four problems: anomaly detection and visual quality inspection on high-speed production lines, verifying product authenticity in the field with standard mobile devices, detecting counterfeit goods through specialised imaging techniques, and identifying counterfeits on online platforms from their digital traces.
The first three share the same difficulty: producing reliable results under uncontrolled conditions on limited hardware. The last is a different class of problem: deciding without any access to the physical product, by evaluating visual and textual signals together.
Sectors where we apply it
Manufacturing
Visual quality inspection, process optimisation and industrial automation.
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Automotive
Surface defect detection, assembly verification and vision models for driver assistance.
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Plastics
Production optimisation, quality control and recycling efficiency.
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Pharmaceuticals
Packaging and serial number verification, retrieval with source attribution and decision trails.
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Food
Production quality control, supply chain and adulteration detection.
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Related posts
Machine vision quality control: from feasibility to the production line
The defect catalogue, the smallest defect size, lighting, the problem of few defective samples, the balance between missed defects and false rejects, and line integration: the questions we ask in order on a visual inspection project.
Vision models at the edge: the latency–accuracy trade-off
Which accuracy loss is acceptable when moving a detection model to real time on constrained hardware through distillation, quantisation and TensorRT.
Other research areas
Generative AI & LLMs
RAG architectures, domain adaptation and fine-tuning, distillation into smaller models, Turkish evaluation sets and on-premise deployment.
AI Agents
Tool use, multi-step planning and multi-agent orchestration; MCP integration, human-in-the-loop workflows and safety guardrails.
MLOps & Model Operations
Evaluation infrastructure, observability, hallucination and regression tracking, continuous retraining.
Reinforcement Learning & Decision Systems
Decision policies that adapt to changing conditions, reward design, training in simulation and safe transfer to the field.
AI Security & Privacy
Robustness against adversarial attacks, prompt injection defence, learning without sharing data, and model confidentiality.
A technical assessment for your AI project
Your project's feasibility, risks and timeline are assessed in a technical consultation.