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 components
• AI-driven approaches versus traditional rule-based visual inspection

Image Acquisition and Preprocessing

• Camera types and optimal image capture configurations
• 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 frameworks: CNNs, U-Net, and YOLO
• Selecting between detection, classification, and segmentation approaches

Defect Detection Model Development

• Preparation of annotated datasets for training
• Training defect classifiers and segmenters
• Evaluating model performance via precision, recall, and F1-score

Deployment in Industrial Settings

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

Performance Tuning and Maintenance

• Managing variable lighting and shifting production conditions
• Strategies for model retraining and continual learning
• Implementation of alerting, logging, and QA reporting integration

Case Studies and Domain Applications

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

Summary and Next Steps