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.

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.

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

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

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

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

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

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.