Brain-Sense™ Technology Platform

Computer Vision for Defect Detection

At the core of Brain-Sense™ is Mindtrace’s advanced Computer Vision AI, designed specifically for industrial environments. Using deep learning and neural networks, the platform analyzes images and video data to detect patterns, identify defects, and drive intelligent action – mirroring human perception but without the limitations of fatigue or inconsistency. Unlike AI technologies that rely on rules-based learning, computer vision learns directly from images resulting in unrivaled accuracy and continuous learning.

Brain-Sense™ Technology Platform

Computer Vision for Defect Detection

At the core of Brain-Sense™ is Mindtrace’s advanced Computer Vision AI, designed specifically for industrial environments. Using deep learning and neural networks, the platform analyzes images and video data to detect patterns, identify defects, and drive intelligent action – mirroring human perception but without the limitations of fatigue or inconsistency. Unlike AI technologies that rely on rules-based learning, computer vision learns directly from images resulting in unrivaled accuracy and continuous learning.

Brain-Sense™ AI Brain Bank

The core of Brain-Sense™ is a Brain Bank of data models that are pretrained to recognize a wide range of manufacturing defects and only require minimal additional training to maximize results for each customer.  

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 features of Brain-Sense™ include:

Few-Shot Detection

Accurate object recognition with minimal training data, reducing data collection and model training time.

Continuous Learning

Enhancing AI models by allowing them to adapt and improve over time, without starting from scratch.

Transfer Learning

Leveraging pre-trained models on new tasks, reducing normal setup time and data requirements.

Unsupervised Learning

Uncovering hidden data patterns and structures without labeled examples, aiding in speed to insight.

Deep Attention Learning

Enhancing model performance by focusing on important features and improving task understanding.

Self-Supervised Learning

Enabling our models to learn from unlabeled data, reducing the need for annotated training images.

Few-Shot Detection

Accurate object recognition with minimal training data, reducing data collection and model training time.

Continuous Learning

Enhancing AI models by allowing them to adapt and improve over time, without starting from scratch.

Transfer Learning

Leveraging pre-trained models on new tasks, reducing normal setup time and data requirements.

Unsupervised Learning

Uncovering hidden data patterns and structures without labeled examples, aiding in speed to insight.

Deep Attention Learning

Enhancing model performance by focusing on important features and improving task understanding.

Self-Supervised Learning

Enabling our models to learn from unlabeled data, reducing the need for annotated training images.

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