AI TRAINING
Computer Vision for Quality Inspection
Deploy vision-based defect detection systems that integrate with your existing production infrastructure.
What it covers
This practitioner-level programme trains manufacturing quality and operations teams to design, build, and maintain computer vision inspection systems. Participants work through the full pipeline: camera and lighting setup, image labelling workflows, model training and iteration, and integration with MES/SCADA systems. The format combines hands-on lab sessions with real defect datasets and guided integration exercises. By the end, teams can independently operate and retrain models as product lines or defect profiles evolve.
What you'll be able to do
- Design a camera and lighting setup appropriate for a specific defect type and production speed
- Build and manage an image labelling pipeline that produces model-ready annotated datasets
- Train, evaluate, and iteratively improve a defect detection model using real production imagery
- Deploy a trained model to an edge device and connect its outputs to a MES or SCADA rejection workflow
- Define KPIs and monitoring dashboards to track model drift and inspection accuracy over time
Topics covered
- Camera selection, lens optics, and structured lighting for industrial inspection
- Image acquisition pipelines and data collection strategies on the production floor
- Annotation tooling and labelling workflows for defect classification and segmentation
- Training object detection and segmentation models (YOLO, Detectron2) on defect datasets
- Model evaluation metrics: precision, recall, false-positive rate in quality contexts
- Iterative retraining and active learning when defect profiles change
- Edge deployment on industrial hardware (NVIDIA Jetson, industrial PCs)
- Integration with MES, SCADA, and PLCs for real-time rejection and alerting
Delivery
Typically delivered as a blended programme over 3-4 weeks: two in-person lab days on-site (or at a partner facility with industrial camera rigs), complemented by guided online modules and weekly coaching calls. Participants bring or are provided with a labelled defect dataset representative of their production context. Hands-on work accounts for roughly 65% of total time. Remote delivery is possible with a hardware kit loan (camera, lighting, edge device) shipped to participants.
What makes it work
- Involve production operators in labelling sessions — their domain knowledge dramatically improves annotation quality
- Establish a closed-loop feedback process so flagged escapes automatically re-enter the training dataset
- Pilot on a single inspection station with high defect volume before scaling across the line
- Secure OT/IT collaboration early to agree on communication protocols and network segmentation for edge devices
Common mistakes
- Collecting images under inconsistent lighting conditions, causing models that perform in the lab but fail on the line
- Labelling too few defect samples per class, leading to high false-positive rates that operators learn to ignore
- Treating model deployment as a one-time event rather than building a retraining loop as new defect types emerge
- Skipping MES/SCADA integration design until late in the project, creating costly rework at the OT boundary
When NOT to take this
This programme is not the right fit for teams that have not yet standardised their physical inspection process — if defect definitions are contested and acceptance criteria are informal, a computer vision model will encode the disagreement and produce unreliable results. Fix the process specification first.
Providers to consider
- DataScientestdatascientest.com →
- Fraunhofer IPA (Applied AI for Manufacturing)www.ipa.fraunhofer.de/en/expertise/robot-and-assistive-systems/machine-vision.html →
- Coursera – Deep Learning Specialization (Computer Vision track)www.coursera.org/specializations/deep-learning →
- Roboflow (vision-specific training and tooling)roboflow.com →
Sources
This training is part of a Data & AI catalog built for leaders serious about execution. Take the free diagnostic to see which trainings your team needs.