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

Introduction to Industrial Computer Vision

• Overview of machine vision systems within manufacturing contexts
• Common defect types: cracks, scratches, misalignments, and missing components
• Comparison of AI-based approaches versus traditional rule-based visual inspection

Image Acquisition and Preprocessing

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

Object Detection and Segmentation Techniques

• Classical methods including thresholding, edge detection, and contour analysis
• Deep learning models such as CNNs, U-Net, and YOLO
• Selecting the appropriate approach: detection, classification, or segmentation

Defect Detection Model Development

• Preparation of annotated datasets
• Training defect classifiers and segmentation models
• Evaluating model performance using precision, recall, and F1-score

Deployment in Industrial Settings

• Hardware considerations involving GPUs, edge devices, and industrial PCs
• Designing real-time inspection pipeline architectures
• Integrating systems with PLCs and broader factory automation frameworks

Performance Tuning and Maintenance

• Adapting to varying lighting and production conditions
• Implementing model retraining and continual learning processes
• Establishing alerting, logging, and QA reporting integration

Case Studies and Domain Applications

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

Summary and Next Steps