Application Hub

Secure face recognition HMI system for personalized user access in industrial environments.

HMI system that uses AI to recognize operators during alerts and show personalized troubleshooting instructions.

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

Detects critical alarm sounds (e.g., fire alarms) and triggers immediate machine shutdown for safety compliance.

Lightweight real-time people counting model based on quantized YOLOX, optimized for edge devices using LiteRT (TensorFlow Lite) .

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

Lightweight semantic segmentation model optimized for real-time street scene analysis on edge devices.

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.

Neural network model for forecasting household electricity consumption using 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.

Efficient CNN model for image classification on mobile and embedded edge devices.

Fast object detection using YOLOX optimized for embedded edge devices.

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

Real-time activity recognition using motion sensor data from wearable fitness devices.

Deep learning model to detect presentation attacks in biometric systems, enhancing security against spoofing attempts.

Speech recognition model for transcribing spoken language into text .

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

A time-series forecasting model designed to predict sensor data patterns in smart buildings, enabling proactive energy management and maintenance.

High-precision OCR application that detects and transcribes text from real-world images with superior accuracy, ideal for industrial and logistics use cases.

Lightweight OCR application that detects and reads text in images using a faster, lower-resource model designed for embedded and edge devices.

Specialized AI-based vision system for detecting surface and structural defects on printed circuit boards (PCBs) during manufacturing inspection.

A lightweight face recognition solution leveraging Edge Impulse’s FOMO model, designed for fast, accurate deployment on edge devices in industrial environments.

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

Robust speaker ID/verification using ECAPA-TDNN embeddings for secure, personalized HMI and analytics.

Advanced transformer-based detector optimized for accurate, real-time edge performance on SECO platforms.

Deep-learning gesture recognition for touchless HMI in dynamic environments.

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.

Predictive modeling for TBM settlement to enhance safety and planning in tunneling projects.

AI-based detection for product recognition and user interaction in smart vending systems.

Facial emotion analysis for adaptive, personalized HMI experiences.

Efficient edge analytics using Qualcomm HW to extract facial attributes for personalization and safety.

Industrial image segmentation and background removal for visual inspection and automation.

Acoustic event detection for machinery monitoring and anomaly prevention in noisy industrial settings.

Flexible model training and validation experience with deployment and inference on SECO edge hardware.

Automated detection and mapping of land-cover changes from satellite or aerial imagery for environmental monitoring and urban planning.

High-accuracy speech transcription optimized for automated documentation and voice-controlled operations in demanding environments.

Energy-efficient, real-time object detection optimized for low-power edge devices with strict battery and thermal constraints.

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

Deep learning solution that classifies human actions in video streams for workflow analysis and operational safety.

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

High-accuracy speech transcription using Wave2Vec for industrial documentation, healthcare automation, and voice-controlled processes.

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.