Master’s in Vision, Metrology & Edge AI Engineering
Online master's programme of 1,900 hours in vision, metrology and edge AI engineering. 12 modules covering industrial machine vision, image processing, dimensional metrology, 3D vision, deep learning for inspection, edge AI hardware and model optimisation, plant integration, industrial MLOps and validation under EMVA 1288, GenICam and VDI/VDE 2634 Part 2, with an assigned technical tutor.
Take the controls.
Run a real engineering trade-off from this field. Move the sliders, watch the model react in real time and beat the challenge.
What you will be able to do
Design industrial vision systems with suitable optics and lighting.
Apply image processing techniques to inspection.
Carry out dimensional measurements with known uncertainty.
Develop deep learning models for defect detection.
Optimise and deploy models on edge hardware.
Integrate vision systems with PLCs and MES.
Validate inspection systems in regulated environments.
Who it's for
- Machine vision engineers.
- Optical metrology specialists.
- Embedded AI engineers.
- Inspection systems integrators.
- Quality engineers.
- Industrial MLOps engineers.
- Engineering or science graduates aiming to enter the automotive sector.
What you'll work with
- EMVA 1288 (camera and image sensor characterisation)
- GenICam (generic camera programming interface)
- VDI/VDE 2634 Part 2 (optical 3D measuring systems)
- JCGM 100:2008 – GUM (guide to the expression of uncertainty in measurement)
- Regulation (EU) 2024/1689 (EU Artificial Intelligence Act)
- ISO 10360-1:2000 (acceptance and verification of CMMs – vocabulary)
- IEC 62443-4-2:2019 (security requirements for IACS components)
- ISO 22514-7:2021 (capability of measurement processes)
- MVTec Halcon
- OpenCV
- NVIDIA TensorRT
- Intel OpenVINO
- Open3D
12 modules · 1900 h
1.1Components of a vision system1.2Industrial CMOS and CCD sensors1.3Optics, focal length and depth of field1.4Lighting techniques: bright field and dark field1.5Backlighting and structured light1.6EMVA 1288 camera standard1.7GigE Vision and USB3 Vision interfaces1.8Triggering, synchronisation and encoders1.9Line-scan cameras for continuous materials1.10Lab: optics selection for an inspection task
2.1Spatial filters and morphology2.2Thresholding and classical segmentation2.3Sub-pixel edge detection2.4Blob analysis and geometric measurements2.5Template matching and pattern search2.6Hough transform2.7Code reading and industrial OCR2.8Distortion correction2.9Programming with OpenCV and Halcon2.10Practical: presence inspection with OpenCV
3.1Intrinsic and extrinsic calibration3.2Telecentric lenses for measurement3.3Measuring diameters and distances3.4Uncertainty according to the GUM3.5Measurement system analysis (MSA)3.6Gage R&R studies3.7Traceability to reference standards3.8Temperature and measurement stability3.9Comparison with coordinate measuring machines3.10Case study: R&R study of an optical gauge
4.1Laser triangulation and profilometers4.2Structured light and fringe projection4.3Stereo and time-of-flight cameras4.4Point clouds and filtering4.5Point cloud registration and CAD alignment4.6Flatness and volume inspection4.7Formats and libraries: PCL and Open3D4.8Accuracy to VDI/VDE 26344.93D vision-guided robots4.10Lab: warpage measurement with a profilometer
5.1Convolutional networks for classification5.2Object detection with YOLO5.3Semantic segmentation of defects5.4Anomaly detection without labelled defects5.5Data augmentation and synthetic data5.6Labelling and dataset management5.7Metrics: precision, recall and ROC curves5.8Explainability with activation maps5.9Managing false rejects5.10Practical: anomaly detector with PatchCore
6.1NVIDIA Jetson architectures6.2Hailo and Coral TPU accelerators6.3Intel processors with OpenVINO6.4FPGAs and smart cameras6.5Power consumption and heat dissipation6.6In-line latency constraints6.7Performance-per-watt comparison6.8Industrial ruggedness and certifications6.9Platform selection by application6.10Case study: sizing edge hardware
7.1Quantisation to INT8 and FP167.2Pruning and knowledge distillation7.3Export to ONNX7.4Optimisation with TensorRT7.5Inference with OpenVINO7.6Latency and throughput benchmarking7.7Pipelines with DeepStream7.8Accuracy loss after optimisation7.9Memory management on devices7.10Lab: deploying YOLO on Jetson
8.1Communication with PLCs over Profinet8.2Publishing results with OPC UA8.3Part rejection and synchronisation8.4Integration with MES and traceability8.5Inspection HMI interfaces8.6Image storage and audit8.7Hybrid edge-cloud architectures8.8Containerisation with Docker8.9Security of vision devices8.10Practical: integrating inspection with a PLC
9.1Life cycle of vision models9.2Data and model versioning9.3Data drift detection9.4Continuous retraining with new data9.5OTA deployment to device fleets9.6Model quality monitoring9.7Validation before releasing versions9.8Governance and documentation9.9EU Artificial Intelligence Act9.10Workshop: retraining pipeline
10.1Inspection of metal surfaces10.2Weld inspection and thermal inspection10.3Electronics: automated optical inspection10.4Food: sorting and foreign bodies10.5Pharmaceuticals: packaging verification10.6Automotive: assembly verification10.7Logistics: reading and dimensioning10.8Textiles and wood: defects in continuous processes10.9Return on investment in vision10.10Case study: AI-based surface inspection
11.1Validation plan for a vision system11.2Tests with master samples11.3Inspection process capability11.4Validation according to GAMP 511.5System risk analysis11.6Technical documentation and traceability11.7Maintenance and recalibration11.8Quality audits11.9Data-driven continuous improvement11.10Practical: inspection validation plan
12.1Definition of the inspection problem12.2Optical and lighting design12.3Data acquisition and labelling12.4AI model development12.5Optimisation and deployment at the edge12.6Integration with the line12.7Metrological and classification validation12.8Conclusions and industrial proposal12.9Master's Thesis report12.10Master's Thesis: defence before a panel
How you'll learn
A 100% online programme on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: you progress at your own pace within each intake, so it fits alongside your job.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is MVTec Halcon, OpenCV, NVIDIA TensorRT, Intel OpenVINO and Open3D, in free, academic or evaluation versions. Exercises are based on real procedures: AI-based defect inspection at the edge, repeatability studies of optical gauges, and model deployment on Jetson. Assessment is continuous by module, plus the final Master's Thesis.
Standards covered: EMVA 1288, GenICam, VDI/VDE 2634 Part 2, JCGM 100:2008 (GUM), Regulation (EU) 2024/1689, ISO 10360-1:2000, IEC 62443-4-2:2019, ISO 22514-7:2021.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Where this takes you
Requirements
- University degree in engineering, science or architecture, or equivalent proven professional experience.
- Internet connection and a computer capable of running the programme software.
- English sufficient to read technical standards.
Ready to start?
Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.
Frequently asked questions
The programme totals 1,900 hours over 12–14 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.
You work with MVTec Halcon, OpenCV, NVIDIA TensorRT, Intel OpenVINO and Open3D, using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.
A SEIUM Institute diploma, awarded on passing the continuous assessment and the Master's Thesis. It is SEIUM's own qualification, not an official state degree; its recognition depends on the programme completed and the regulations of each country.
Yes. Modules passed in a course or postgraduate diploma in the same area are recognised in the higher programme. Ask about the recognition policy when you enrol.
A degree in engineering, science or architecture (or equivalent professional experience), a computer able to run the software and English sufficient to read technical standards.
Start dates are set by SEIUM's faculty for each intake. Our admissions team will confirm the next available date when you apply.
Related programmes
Master’s in End-Effectors and Workholding Systems Engineering
Online master's programme of 1,900 hours in end-effector and workholding systems engineering. 12 modules covering grip mechanics, mechanical and vacuum grippers, process tools, tool changers, fixtures,…
Master’s in Industrial Exoskeletons and Ergonomics Engineering
Online master's programme of 1,900 hours in industrial exoskeletons and ergonomics engineering. 12 modules covering industrial exoskeletons, human biomechanics, ergonomics and musculoskeletal disorders, mechanical design, actuation…
Master’s in Industrial and Economic MPC Engineering
Online master's programme of 1,900 hours in industrial and economic MPC engineering, structured in 12 modules covering identification for MPC, linear, economic, nonlinear, robust and stochastic…
More in Robotics, Automation & Control
All Robotics, Automation & Control programmes → · How to apply · Fees & financing · Scholarships