Real-time face detection for industrial HMI applications.

AI-assisted HMI that detects operator presence during machine alerts and provides contextual troubleshooting information.

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

Real-time detection of critical alarm and emergency sounds for industrial safety applications.

Real-time people detection and counting for industrial and monitoring applications.

Lightweight and quantized PoseNet model for real-time multi-person pose estimation on edge hardware.

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

Efficient single-camera depth estimation using quantized MiDaS v2 model optimized for edge AI deployment.

High-resolution neural network for accurate keypoint detection in human pose estimation tasks.

Short-term electricity consumption forecasting based on historical time-series data.

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.

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

Detects face presentation attacks and verifies liveness from RGB images to strengthen biometric authentication systems..

Deep learning system to upscale and enhance low-resolution images while preserving fine details.

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.

Detects faces and performs face recognition using a lightweight Edge Impulse pipeline designed for edge devices.

A training pipeline for customizing YAMNet to recognize new environmental or industrial sounds.

Identifies or verifies speakers from voice recordings using robust ECAPA-TDNN speaker embeddings.

Recognizes hand gestures over time to enable touchless human–machine interaction 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.

Predicts ground settlement around tunnel boring operations using geotechnical and TBM operational data.

Detects and maps land-cover changes from multi-temporal satellite or aerial imagery for environmental monitoring and urban analysis.

AI vision application to detect human falls in real time for safety, elderly care, and industrial risk monitoring.

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

Guided training environment for creating and preparing custom Face Recognition models, with optimized deployment and inference on SECO hardware.

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.