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Course Outline
Introduction to Industrial Computer Vision
- Overview of machine vision systems 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 settings.
- Noise reduction, contrast enhancement, and normalization techniques.
- Data augmentation strategies for training robustness.
Object Detection and Segmentation Techniques
- Classical approaches: thresholding, edge detection, and contours.
- Deep learning methods: CNNs, U-Net, and YOLO.
- Criteria for choosing between detection, classification, and segmentation.
Defect Detection Model Development
- Preparing 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.
- Designing real-time inspection pipeline architecture.
- Integration with PLCs and factory automation systems.
Performance Tuning and Maintenance
- Adapting to changing lighting and production conditions.
- Model retraining and continual learning strategies.
- Integrating 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 pharmaceutical and food industries.
Summary and Next Steps
Requirements
- Experience with machine learning or computer vision concepts.
- Proficiency in Python programming.
- Basic understanding of quality control or industrial automation.
Target Audience
- QA teams.
- Automation engineers.
- Computer vision developers.
14 Hours
