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, missing parts
• AI-driven inspection vs. traditional rule-based methods

Image Acquisition and Preprocessing

• Camera types and optimal image capture configurations
• Noise reduction, contrast enhancement, and normalization techniques
• Data augmentation strategies to ensure training robustness

Object Detection and Segmentation Techniques

• Traditional approaches (thresholding, edge detection, contour analysis)
• Deep learning methods: CNNs, U-Net, YOLO
• Selecting the right approach: detection, classification, or segmentation

Defect Detection Model Development

• Preparing and structuring annotated datasets
• Training classifiers and segmenters for specific defects
• Evaluating model performance: precision, recall, and F1-score

Deployment in Industrial Settings

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

Performance Tuning and Maintenance

• Managing variable lighting and shifting production conditions
• Model retraining strategies and continuous learning
• Setting up alerting, logging, and QA reporting integrations

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