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

Introduction to Industrial Computer Vision

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

Image Acquisition and Preprocessing

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

Object Detection and Segmentation Techniques

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

Defect Detection Model Development

• Preparation of annotated datasets.
• Training defect classifiers and segmenters.
• 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 broader factory automation systems.

Performance Tuning and Maintenance

• Managing varying lighting conditions and production fluctuations.
• Strategies for model retraining and continual learning.
• Implementation of 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