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

Introduction to Industrial Computer Vision

• An overview of machine vision applications in manufacturing
• Common defect types: cracks, scratches, misalignments, and missing components
• Comparing AI-driven methods with traditional rule-based visual inspection

Image Acquisition and Preprocessing

• Camera classifications and optimal image capture configurations
• Techniques for noise reduction, contrast enhancement, and data normalization
• Applying data augmentation to enhance model robustness during training

Object Detection and Segmentation Techniques

• Foundational methods: thresholding, edge detection, and contour analysis
• Advanced deep learning approaches: CNNs, U-Net, and YOLO
• Selecting the appropriate strategy among detection, classification, and segmentation

Defect Detection Model Development

• Curating and preparing annotated datasets
• Training defect classifiers and segmentation models
• Evaluating model performance using precision, recall, and F1-score metrics

Deployment in Industrial Settings

• Hardware requirements: GPUs, edge devices, and industrial PCs
• Designing the architecture for real-time inspection pipelines
• Integrating systems with PLCs and factory automation infrastructure

Performance Tuning and Maintenance

• Adapting to varying lighting conditions and production changes
• Implementing model retraining and continual learning strategies
• Establishing alerting, logging, and QA reporting workflows

Case Studies and Domain Applications

• Defect detection in automotive assembly and welding processes
• Surface inspection techniques for electronics and semiconductors
• Verifying labels and packaging in pharmaceutical and food industries

Summary and Next Steps