Industrial Computer Vision with AI: Defect Detection and Visual Inspection

• 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

Industrial computer vision with AI is transforming how manufacturers and QA teams detect surface defects, verify part conformity, and automate visual inspection processes.

This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level QA teams, automation engineers, and developers who wish to design and implement computer vision systems for defect detection and inspection using AI techniques.

By the end of this training, participants will be able to:

• Understand the architecture and components of industrial vision systems.
• Build AI models for visual defect detection using deep learning.
• Integrate real-time inspection pipelines with industrial cameras and devices.
• Deploy and optimize AI-powered inspection systems for production environments.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Introduction to Industrial Computer Vision

• Overview of machine vision systems in manufacturing
• Typical defects: cracks, scratches, misalignments, missing components
• AI vs traditional rule-based visual inspection

Image Acquisition and Preprocessing

• Camera types and image capture settings
• Noise reduction, contrast enhancement, and normalization
• Data augmentation for 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 pharma and food

Summary and Next Steps