Cursusaanbod

Introduction to Industrial Computer Vision

  • Overview of machine vision systems in manufacturing
  • Typical defects: cracks, scratches, misalignments, missing components
  • AI vs traditional rule-based visual inspection

Image Acquisition and Preprocessing

  • Camera types and image capture settings
  • Noise reduction, contrast enhancement, and normalization
  • Data augmentation for training robustness

Object Detection and Segmentation Techniques

  • Classical approaches (thresholding, edge detection, contours)
  • Deep learning methods: CNNs, U-Net, YOLO
  • Choosing between detection, classification, and segmentation

Defect Detection Model Development

  • Preparing annotated datasets
  • Training defect classifiers and segmenters
  • Model evaluation: precision, recall, F1-score

Deployment in Industrial Settings

  • Hardware considerations: GPUs, edge devices, industrial PCs
  • Real-time inspection pipeline architecture
  • Integration with PLCs and factory automation systems

Performance Tuning and Maintenance

  • Handling changing lighting and production conditions
  • Model retraining and continual learning
  • Alerting, logging, and QA reporting integration

Case Studies and Domain Applications

  • Defect detection in automotive assembly and welding
  • Surface inspection in electronics and semiconductors
  • Label and packaging verification in pharma and food

Summary and Next Steps

Vereisten

  • Experience with machine learning or computer vision concepts
  • Familiarity with Python programming
  • Basic understanding of quality control or industrial automation

Audience

  • QA teams
  • Automation engineers
  • Computer vision developers
 14 Uren

Leveringsopties

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