Brain-Sense™ Technology Platform
Computer Vision for Defect Detection
Brain-Sense™ is the proprietary Mindtrace AI platform for manufacturing defect detection. It provides manufacturers with fully automated, highly accurate, defect detection with significant advantages:
- Detects defects faster and more accurately, reducing escapes and improving yield
- Operates 24/7/365 without fatigue
- Delivers cost savings compared to manual inspections
Brain-Sense™ Technology Platform
Computer Vision for Defect Detection
Brain-Sense™ is the proprietary Mindtrace AI platform for manufacturing defect detection. It provides manufacturers with fully automated, highly accurate, defect detection with significant advantages:
- Detects defects faster and more accurately, reducing escapes and improving yield
- Operates 24/7/365 without fatigue
- Delivers cost savings compared to manual inspections
With Mindtrace
Fewer Images
Cost Reduction
Gage R&R Score
Brain-Sense™ AI Brain Bank
The core of Brain-Sense™ is a Brain Bank of AI data models that are pretrained to recognize a wide range of manufacturing defects. As a result these “brains” can be rapidly customized for each customer’s unique needs with minimal additional.
The Brain-sense™ platform provides manufacturers with rapid pass/fail decisions and uses less processing power than other computer vision platforms, while still delivering faster start-ups and more flexible change management than traditional rules-based, machine vision platforms.
Key Capabilities of Brain-Sense™
Brain-Sense™ incorporates powerful, proven AI techniques into a unified, practical platform for manufacturing defect detection.
Fast & Efficient Training
- Few-Shot Detection – Achieves high accuracy with as few as 20 images, minimizing data collection and training time.
- Transfer Learning – Adapts pre-trained AI “brains” to new tasks quickly, accelerating deployment.
Smarter Over Time
- Continuous Learning – Models adapt to new parts, processes, and defects without retraining from scratch.
- Self-Supervised Learning – Improves performance using unlabelled data, reducing reliance on expensive annotations.
Deeper Quality Insights
- Unsupervised Learning – Detects hidden patterns and insights beyond predefined rules.
- Deep Attention Learning – Focuses on the most relevant features of each part, improving accuracy and defect recognition.
Fast & Efficient Training
- Few-Shot Detection – Achieves high accuracy with as few as 20 images, minimizing data collection and training time.
- Transfer Learning – Adapts pre-trained AI “brains” to new tasks quickly, accelerating deployment.
Fast & Efficient Training
- Few-Shot Detection – Achieves high accuracy with as few as 20 images, minimizing data collection and training time.
- Transfer Learning – Adapts pre-trained AI “brains” to new tasks quickly, accelerating deployment.
Smarter Over Time
- Continuous Learning – Models adapt to new parts, processes, and defects without retraining from scratch.
- Self-Supervised Learning – Improves performance using unlabelled data, reducing reliance on expensive annotations.
Smarter Over Time
- Continuous Learning – Models adapt to new parts, processes, and defects without retraining from scratch.
- Self-Supervised Learning – Improves performance using unlabelled data, reducing reliance on expensive annotations.
Deeper Quality Insights
- Unsupervised Learning – Detects hidden patterns and insights beyond predefined rules.
- Deep Attention Learning – Focuses on the most relevant features of each part, improving accuracy and defect recognition.
Deeper Quality Insights
- Unsupervised Learning – Detects hidden patterns and insights beyond predefined rules.
- Deep Attention Learning – Focuses on the most relevant features of each part, improving accuracy and defect recognition.