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

Introduction to Industrial Computer Vision

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

Image Acquisition and Preprocessing

• Camera types and optimal image capture settings
• Noise reduction, contrast enhancement, and normalization techniques
• Data augmentation strategies for enhancing training robustness

Object Detection and Segmentation Techniques

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

Defect Detection Model Development

• Preparation of annotated datasets
• Training defect classifiers and segmentation models
• Model evaluation metrics: precision, recall, and F1-score

Deployment in Industrial Settings

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

Performance Tuning and Maintenance

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

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 sectors

Summary and Next Steps