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Course Outline
Introduction to Industrial Computer Vision
- Examination of machine vision systems within manufacturing environments
- Common defect classifications: fractures, surface scratches, positional misalignments, and absent components
- Comparative analysis of artificial intelligence approaches versus traditional rule-based visual inspection methodologies
Image Acquisition and Preprocessing
- Specifications for camera types and image capture parameters
- Techniques for noise reduction, contrast enhancement, and data normalization
- Application of data augmentation to improve model robustness for government testing standards
Object Detection and Segmentation Techniques
- Foundational methods including thresholding, edge detection, and contour analysis
- Advanced deep learning architectures: Convolutional Neural Networks (CNNs), U-Net, and YOLO
- Criteria for selecting between object detection, classification, and segmentation tasks
Defect Detection Model Development
- Procedures for preparing annotated datasets
- Training processes for defect classifiers and semantic segmenters
- Evaluation metrics: precision, recall, and F1-score
Deployment in Industrial Settings
- Hardware infrastructure considerations: GPUs, edge computing devices, and industrial personal computers
- Architecture for real-time inspection pipelines
- Integration protocols with Programmable Logic Controllers (PLCs) and factory automation systems for government supply chain compliance
Performance Tuning and Maintenance
- Management strategies for varying lighting conditions and production variables
- Protocols for model retraining and continual learning updates
- Integration of alert systems, logging mechanisms, and Quality Assurance (QA) reporting frameworks
Case Studies and Domain Applications
- Defect detection applications in automotive assembly and welding processes
- Surface inspection requirements for electronics and semiconductor manufacturing sectors supporting government contracts
- Label verification and packaging compliance in pharmaceutical and food industries
Summary and Next Steps
Requirements
- Demonstrated proficiency in machine learning or computer vision methodologies
- Competency in Python software development
- Foundational knowledge of quality assurance protocols and industrial automation systems, suitable for government applications
Target Audience
- Quality assurance personnel
- Automation engineering staff
- Computer vision specialists
14 Hours
