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

Foundations of Industrial Computer Vision

• Perspective on machine vision systems within manufacturing
• Common defects: cracks, scratches, misalignments, absent components
• AI compared to conventional rule-based visual inspection

Image Acquisition and Preprocessing

• Camera varieties and image capture configurations
• Noise suppression, contrast improvement, and normalization
• Data augmentation to ensure training robustness

Object Detection and Segmentation Methods

• Traditional techniques (thresholding, edge detection, contours)
• Deep learning approaches: CNNs, U-Net, YOLO
• Selecting between detection, classification, and segmentation

Developing Defect Detection Models

• Preparing annotated datasets
• Training defect classifiers and segmenters
• Model assessment: precision, recall, F1-score

Deployment in Industrial Contexts

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

Performance Optimization and Upkeep

• Managing variable lighting and production conditions
• Model retraining and continuous learning
• Alerting, logging, and integration with QA reporting

Case Studies and Sector-Specific Applications

• Defect identification in automotive assembly and welding
• Surface inspection in electronics and semiconductors
• Label and packaging verification in pharmaceuticals and food

Conclusion and Future Directions