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

Introduction to Industrial Computer Vision

• An overview of machine vision systems within the manufacturing sector
• Common defects including cracks, scratches, misalignments, and missing components
• A comparison between AI and traditional rule-based visual inspection

Image Acquisition and Preprocessing

• Types of cameras and optimal image capture settings
• Techniques for noise reduction, contrast enhancement, and normalization
• Using data augmentation to ensure training robustness

Object Detection and Segmentation Techniques

• Conventional methods such as thresholding, edge detection, and contour analysis
• Deep learning approaches including CNNs, U-Net, and YOLO
• Determining the best fit among detection, classification, and segmentation

Defect Detection Model Development

• Preparing and annotating datasets
• Training defect classifiers and segmentation models
• Evaluating models based on precision, recall, and F1-score

Deployment in Industrial Settings

• Hardware requirements such as GPUs, edge devices, and industrial PCs
• Designing real-time inspection pipeline architectures
• Integrating with PLCs and broader factory automation systems

Performance Tuning and Maintenance

• Adapting to varying lighting and production conditions
• Implementing model retraining and continual learning strategies
• Setting up alerting, logging, and QA reporting integration

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