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.

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

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.

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

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

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.

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.

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

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

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.

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.