Industrial Computer Vision with AI: Defect Detection and Visual Inspection Training Course

Introduction to Industrial Computer Vision

• Snapshot of machine vision systems in the manufacturing sector
• Common defects: fractures, abrasions, misalignments, absent parts
• AI versus conventional rule-based visual inspection methods

Image Capture and Preprocessing

• Camera varieties and image acquisition configurations
• Techniques for noise mitigation, contrast improvement, and normalization
• Data augmentation strategies to enhance training resilience

Object Detection and Segmentation Strategies

• Traditional techniques (thresholding, edge detection, contour analysis)
• Deep learning approaches: CNNs, U-Net, YOLO
• Selecting the right balance between detection, classification, and segmentation

Developing Defect Detection Models

• Curating and preparing annotated datasets
• Training classifiers and segmenters for defects
• Assessing model performance: precision, recall, F1-score

Deployment in Industrial Contexts

• Hardware factors: GPUs, edge devices, industrial PCs
• Design of real-time inspection pipeline architectures
• Connecting with PLCs and factory automation frameworks

Performance Optimization and Maintenance

• Managing shifts in lighting and production conditions
• Model retraining and continuous learning strategies
• Integration of alerting, logging, and QA reporting workflows

Case Studies and Sector-Specific Applications

• Defect detection in automotive assembly and welding processes
• Surface inspection in electronics and semiconductor manufacturing
• Verification of labels and packaging in pharmaceutical and food industries

Conclusion and Future Directions