Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Introduction to Industrial Computer Vision
- Overview of machine vision systems in manufacturing.
- Common defects: cracks, scratches, misalignments, and missing components.
- AI compared to traditional rule-based visual inspection.
Image Acquisition and Preprocessing
- Camera types and image capture settings.
- Noise reduction, contrast enhancement, and normalization.
- Data augmentation for improved training robustness.
Object Detection and Segmentation Techniques
- Classical approaches (thresholding, edge detection, contours).
- Deep learning methods: CNNs, U-Net, YOLO.
- Choosing between detection, classification, and segmentation.
Defect Detection Model Development
- Preparing annotated datasets.
- Training defect classifiers and segmenters.
- Model evaluation: precision, recall, F1-score.
Deployment in Industrial Settings
- Hardware considerations: GPUs, edge devices, industrial PCs.
- Real-time inspection pipeline architecture.
- Integration with PLCs and factory automation systems.
Performance Tuning and Maintenance
- Handling changing lighting and production conditions.
- Model retraining and continual learning.
- Alerting, logging, and QA reporting integration.
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.
- Familiarity with Python programming.
- Basic understanding of quality control or industrial automation.
Audience
- QA teams.
- Automation engineers.
- Computer vision developers.
14 Hours
