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Duration 14 hours
Course Outline
Introduction to Industrial Computer Vision
- Overview of machine vision systems in manufacturing
- Common defects: cracks, scratches, misalignments, missing components
- AI compared to traditional rule-based visual inspection
Image Acquisition and Preprocessing
- Camera types and image capture configurations
- Noise reduction, contrast enhancement, and normalization
- Data augmentation to ensure training robustness
Object Detection and Segmentation Techniques
- Classic methods (thresholding, edge detection, contours)
- Deep learning approaches: CNNs, U-Net, YOLO
- Selecting between detection, classification, and segmentation
Defect Detection Model Development
- Preparing annotated datasets
- Training defect classifiers and segmenters
- Model assessment: precision, recall, F1-score
Deployment in Industrial Settings
- Hardware considerations: GPUs, edge devices, industrial PCs
- Architecture of real-time inspection pipelines
- Integration with PLCs and factory automation systems
Performance Tuning and Maintenance
- Managing changing lighting and production conditions
- Model retraining and continual learning
- Integration of alerting, logging, and QA reporting
Case Studies and Domain Applications
- Defect detection in automotive assembly and welding
- Surface inspection in electronics and semiconductors
- Label and packaging verification in pharma and food
Summary and Next Steps
Requirements
- Background in machine learning or computer vision principles
- Proficiency in Python programming
- Foundational knowledge of quality control or industrial automation
Audience
- Quality assurance teams
- Automation engineers
- Computer vision developers
Testimonials (1)
How to use and explore AI for daily works
