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 defects: cracks, scratches, misalignments, and missing parts
• Comparing AI approaches with traditional rule-based visual inspection

Image Acquisition and Preprocessing

• Camera types and image capture configurations
• Techniques for noise reduction, contrast enhancement, and normalization
• Using data augmentation to improve training robustness

Object Detection and Segmentation Techniques

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

Defect Detection Model Development

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

Deployment in Industrial Settings

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

Performance Tuning and Maintenance

• Adapting to fluctuating lighting and production conditions
• Model retraining and implementing continual learning
• Setting up 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 components
• Label and packaging verification in pharmaceutical and food industries

Summary and Next Steps