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.
Duration 14 hours
Course Outline
Introduction to Industrial Computer Vision
- Fundamentals of machine vision applications in manufacturing processes
- Common anomalies: fractures, abrasions, positional deviations, absent elements
- Artificial intelligence methodologies versus conventional rule-based inspection protocols
Image Acquisition and Preprocessing
- Sensor specifications and image capture parameter configuration
- Noise attenuation, contrast adjustment, and pixel normalization
- Data augmentation strategies to enhance model robustness
Object Detection and Segmentation Techniques
- Legacy methods: thresholding, edge mapping, and contour analysis
- Deep learning architectures: CNNs, U-Net, and YOLO
- Selection criteria among detection, classification, and segmentation tasks
Defect Detection Model Development
- Compilation of annotated datasets for training
- Training algorithms for defect classification and segmentation
- Model validation metrics: precision, recall, and F1-score
Deployment in Industrial Settings
- Hardware requirements: GPUs, edge computing units, and industrial PCs
- Architecture of real-time inspection workflows
- Interfacing with PLCs and factory automation infrastructure
Performance Tuning and Maintenance
- Managing variability in lighting conditions and production parameters
- Model retraining strategies and continuous learning mechanisms
- Implementation of alert systems, logging protocols, and QA reporting integration
Case Studies and Domain Applications
- Defect identification in automotive assembly and welding operations
- Surface quality assessment in electronics and semiconductor production
- Verification of labels and packaging in pharmaceutical and food industries
Summary and Next Steps
Requirements
- Practical experience with machine learning principles or computer vision concepts
- Proficiency in Python programming
- Foundational knowledge of quality control standards or industrial automation
Audience
- Quality Assurance teams
- Automation engineers
- Computer vision developers
