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 environmental sound classifier optimized for low-power edge devices using MobileNet v1.

Lightweight anomaly detector for time series data using Isolation Forest, designed for edge environments.

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.

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.

Lightweight on-device AI agent for natural interaction and local decision-making without cloud.

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

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

Transcribes spoken audio into text using Wav2Vec2 for edge-based speech recognition 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.