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Course Outline
Introduction to Industrial Computer Vision
- Overview of machine vision applications in manufacturing
- Common defects: cracks, scratches, misalignments, and missing components
- Comparing AI approaches with traditional rule-based visual inspection
Image Acquisition and Preprocessing
- Camera types and image capture configurations
- Noise reduction, contrast enhancement, and normalization techniques
- Data augmentation to improve training robustness
Object Detection and Segmentation Techniques
- Classical methods: thresholding, edge detection, and contour analysis
- Deep learning approaches: CNNs, U-Net, and YOLO
- Selecting between detection, classification, and segmentation strategies
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 for real-time inspection pipelines
- Integration with PLCs and factory automation systems
Performance Tuning and Maintenance
- Adapting to variations in lighting and production conditions
- Model retraining and continual learning processes
- Integration of alerting, logging, and QA reporting
Case Studies and Domain Applications
- Defect detection in automotive assembly and welding
- Surface inspection for electronics and semiconductors
- Label and packaging verification in pharmaceutical and food industries
Summary and Next Steps
Requirements
- Prior experience with machine learning or computer vision concepts
- Proficiency in Python programming
- Fundamental knowledge of quality control or industrial automation
Target Audience
- Quality assurance teams
- Automation engineers
- Computer vision developers
14 Hours
