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

Introduction to Industrial Computer Vision

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

Image Acquisition and Preprocessing

• Camera types and optimal image capture configurations
• Techniques for noise reduction, contrast enhancement, and normalization
• Data augmentation strategies to enhance model robustness

Object Detection and Segmentation Techniques

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

Defect Detection Model Development

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

Deployment in Industrial Settings

• Hardware considerations including GPUs, edge devices, and industrial PCs
• Designing real-time inspection pipeline architectures
• Integration with PLCs and factory automation systems

Performance Tuning and Maintenance

• Adapting to varying lighting conditions and production changes
• Model retraining and continual learning strategies
• Integration of alerting, logging, and QA reporting workflows

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