Real-time face detection for industrial HMI applications.

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

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.

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.

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.

Detects and locates objects in images using the efficient EfficientDet architecture optimized for edge deployment.