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

Fundamentals of Industrial Computer Vision

• Overview of machine vision systems within manufacturing contexts
• Common defect types: cracks, scratches, misalignments, and missing parts
• Comparing AI-based approaches with traditional rule-based visual inspection

Image Capture and Preprocessing

• Camera varieties and image acquisition parameters
• Techniques for noise reduction, contrast improvement, and normalization
• Data augmentation strategies to enhance training robustness

Object Detection and Segmentation Methods

• Classical techniques: thresholding, edge detection, and contour analysis
• Deep learning architectures: CNNs, U-Net, and YOLO
• Selecting between detection, classification, and segmentation strategies

Developing Defect Detection Models

• Preparing annotated datasets for training
• Training defect classifiers and segmentation models
• Model assessment using precision, recall, and F1-score

Industrial Deployment Strategies

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

Performance Optimization and Maintenance

• Adapting to varying lighting and production conditions
• Model retraining and continual learning processes
• Implementation of alerting, logging, and QA reporting integration

Case Studies and Industry Applications

• Defect detection in automotive assembly and welding processes
• Surface inspection for electronics and semiconductor manufacturing
• Verification of labels and packaging in pharmaceutical and food sectors

Conclusions and Future Directions