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

Introduction to Industrial Computer Vision

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

Image Acquisition and Preprocessing

• Camera types and image capture settings
• Techniques for noise reduction, contrast enhancement, and normalization
• Data augmentation to enhance 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

• Preparation of 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

• Managing variable lighting and production conditions
• Model retraining and continual learning strategies
• Integration of alerting, logging, and QA reporting

Case Studies and Domain Applications

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

Summary and Next Steps