Real-time AI-powered vision system for detecting defects in manufacturing environments

Real-time semantic segmentation for understanding roads, vehicles, pedestrians, and other elements in visual scenes.

Real-time image classification for recognizing visual categories on edge devices.

Detects and locates multiple object categories in images and video streams using the lightweight YOLOX architecture.

AI-powered image analysis tool for defect detection in industrial environments, with a guided journey for customizing new defect types.

Classifies physical activities in real time from wearable motion sensor data.

Detects and recognizes text in complex real-world images using a high-accuracy OCR pipeline for edge applications.

Detects and recognizes text in images using a lightweight OCR pipeline optimized for edge devices.

Detects manufacturing defects in PCB images for automated visual quality inspection on edge devices.

On-device fine-tuning/validation of ResNet classifiers—customize without cloud dependency.

Detects products in smart vending environments to support automated product recognition and inventory workflows.

Classifies human actions from video sequences to support activity understanding, workflow analysis, and safety applications.

Energy-efficient object detection optimized for edge platforms with power and thermal constraints.

Bridging the Field and Digital: Universal Connectivity, Zero Latency, and On-Device Machine Learning for Industry 5.0.

Detects and locates objects in images using the efficient EfficientDet architecture optimized for edge deployment.