Industrial Computer Vision with AI: Defect Detection and Visual Inspection Training Course

Introduction to Industrial Computer Vision

• An overview of machine vision systems within the manufacturing sector.
• Common defects encountered: cracks, scratches, misalignments, and missing components.
• A comparison between AI-driven and traditional rule-based visual inspection methods.

Image Acquisition and Preprocessing

• Selection of camera types and optimal image capture settings.
• Techniques for noise reduction, contrast enhancement, and image normalization.
• Applying data augmentation to enhance model training robustness.

Object Detection and Segmentation Techniques

• Classical approaches such as thresholding, edge detection, and contour analysis.
• Deep learning methodologies including CNNs, U-Net, and YOLO.
• Strategic selection among detection, classification, and segmentation tasks.

Defect Detection Model Development

• Preparation and structuring of annotated datasets.
• Training effective defect classifiers and segmenters.
• Model evaluation using key metrics: precision, recall, and F1-score.

Deployment in Industrial Settings

• Hardware considerations involving GPUs, edge devices, and industrial PCs.
• Architecting real-time inspection pipelines.
• Integration with PLCs and broader factory automation systems.

Performance Tuning and Maintenance

• Adapting to changing lighting conditions and production variables.
• Implementing model retraining and continual learning strategies.
• Establishing alerting, logging, and QA reporting integrations.

Case Studies and Domain Applications

• Defect detection applications in automotive assembly and welding processes.
• Surface inspection techniques for electronics and semiconductor manufacturing.
• Verification of labels and packaging in pharmaceutical and food industries.

Summary and Next Steps