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Duration 14 hours
Course Outline
Introduction to Industrial Computer Vision
- Overview of machine vision systems within manufacturing
- Common defects: fractures, scuffs, misalignments, absent components
- AI compared to conventional rule-based visual inspection
Image Acquisition and Preprocessing
- Camera categories and image capture parameters
- Noise mitigation, contrast improvement, and normalization
- Data augmentation to ensure model robustness
Object Detection and Segmentation Methods
- Conventional techniques (thresholding, edge detection, contours)
- Deep learning approaches: CNNs, U-Net, YOLO
- Selecting between detection, classification, and segmentation
Developing Flaw Detection Models
- Creating annotated datasets
- Training flaw classifiers and segmenters
- Model assessment: precision, recall, F1-score
Deployment in Industrial Environments
- Hardware factors: GPUs, edge devices, industrial PCs
- Architecture of real-time inspection pipelines
- Integration with PLCs and factory automation systems
Performance Optimization and Maintenance
- Managing varying lighting and production conditions
- Model retraining and continuous learning
- Integration of alerting, logging, and QA reporting
Case Studies and Industry Applications
- Flaw detection in automotive assembly and welding
- Surface examination in electronics and semiconductors
- Label and packaging verification in pharmaceutical and food sectors
Recap and Future Directions
Requirements
- Prior experience with machine learning or computer vision principles
- Knowledge of Python programming
- Fundamental grasp of quality control or industrial automation
Target Audience
- Quality Assurance teams
- Automation engineers
- Computer vision developers
Testimonials (1)
How to use and explore AI for daily works
