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

Introduction to Industrial Computer Vision

• An overview of machine vision systems in manufacturing contexts
• Common defects: cracks, scratches, misalignments, missing components
• AI versus traditional rule-based visual inspection methods

Image Acquisition and Preprocessing

• Camera types and optimal image capture settings
• Techniques for noise reduction, contrast enhancement, and normalization
• Data augmentation strategies to improve training robustness

Object Detection and Segmentation Techniques

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

Defect Detection Model Development

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

Deployment in Industrial Settings

• Hardware considerations: GPUs, edge devices, industrial PCs
• Architecture of real-time inspection pipelines
• Integration with PLCs and factory automation systems

Performance Tuning and Maintenance

• Adapting to changing lighting and production conditions
• Model retraining strategies and continual learning
• Integration of alerting, logging, and QA reporting

Case Studies and Domain Applications

• Defect detection in automotive assembly and welding
• Surface inspection in electronics and semiconductor manufacturing
• Label and packaging verification in pharmaceutical and food industries

Summary and Next Steps