Intel® Edge Software Hub

Download prevalidated software to learn, develop, and test your solutions for the edge.

Use Cases and Reference Implementations

Download a reference implementation that includes preconfigured software necessary to build a complete sample application. Select use cases to see real-world applications for the edge.

Reference Implementation

Textile Defect Classifier 

Use classification models and computer vision algorithms, optimized by Intel® Distribution of OpenVINO™ toolkit, to identify defects in a textile manufacturing or printing facility to improve the accuracy of quality assurance. 

Documentation

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Reference Implementation

Social Distancing Detection for Retail Settings

Create an end-to-end pipeline to detect social distancing, a COVID-19 preventive measure. Apply deep learning models from the Intel® Distribution of OpenVINO™ toolkit to measure the distance between people based on the input from multiple camera feeds. 

Documentation

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Reference Implementation

Intel® Connected Logistics Platform (Intel® CLP)

Use battery-operated, smart wireless sensor devices at the edge that communicate to mobile or stationary gateways with cloud connectivity and provide real-time visibility into the location, condition, and security of an asset.

Documentation

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Reference Implementation

Wireless Network-Ready Intelligent Traffic Management

Consolidate visual intelligence and wireless workloads at traffic intersections. This container is hosted in an Open Network Edge Services Software (OpenNESS) edge node, which contains the necessary software stacks to host a 5G RAN.

Documentation

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Reference Implementation

Intelligent Traffic Management 

Monitor traffic intersections via IP cameras to optimize traffic flow. Detect vehicles and pedestrians, record vehicle types and counts, calculate velocity and acceleration, and more.

Documentation

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Reference Implementation

Driver Management

Use computer vision, deep learning, and edge networking to develop a solution for managing driver behavior and alertness. Provide real-time alerts to the driver and fleet manager, plus long-term analytics about drivers, vehicles, and routes.

Documentation

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Reference Implementation

Automated Checkout 

Use computer vision to detect products that are removed from a cooler or cabinet without the mechanical pusher or robotic arms.

Documentation

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Reference Implementation

Real-Time Sensor Fusion for Loss Prevention

Combine data from point-of-sale systems, scales, cameras, and RFID readers to prevent loss at checkout.

Documentation

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Reference Implementation

Rotor Bearing Defect Detector

Predict performance issues with manufacturing equipment. Perform local or cloud analytics on any issues found and predict when failures might arise.

Documentation

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Use Case

Connect Edge Devices to Microsoft Azure* IoT​

Simplify Azure* IoT Edge deployments. Install a web-based application that provides an out-of-box experience to securely connect your Intel device to Microsoft Azure* IoT Hub and Azure* IoT Central. Create resources and enable configuration and monitoring of edge AI and use case deployments.

You are responsible for payment of all third-party charges, including payment for use of Microsoft Azure* services.​

Documentation

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Use Case

Universal Wellpad Controller

Use an open application reference middleware to accelerate software modernization strategies for Process Control and Process Optimization in the upstream oil and gas industry. Add new protocols, data analysis, and other capabilities to meet specific application needs.

Documentation

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Use Case

Amazon Web Services (AWS)* Cloud to Edge Pipeline

Single-click deployment of a ready-to-use cloud to edge inferencing pipeline that uses AWS IoT Greengrass and OpenVINO™ toolkit on the edge, and AWS IoT in the cloud. Includes sample AWS IoT Greengrass Lambda for image classification and object detection.​

You are responsible for payment of all third-party charges, including payment for use of Amazon Web Services (AWS).

Documentation

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Use Case

Machine Asset Monitoring and Control

Apply AI to process automation for better asset monitoring and control. Control processes with real-time capabilities and optimize process efficiency.

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Use Case

Compressor Leak and Surge Detection

Detect a compressor leak or surge by applying AI to time-series data. Identify surges that are against the direction of flow or an oscillating flow in both directions.

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Use Case

Electrical Fault Detection

Detect a fault or fault current in an electric power system.

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Use Case

Equipment Failure Detection

Apply machine learning to time-series data to detect potential equipment failures and provide recommended actions. Includes a prevalidated set of components in a ready-to-use, containerized architecture.

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Use Case

Road Infrastructure

Create innovative solutions for traffic management, smart parking, geospatial mapping, road condition monitoring, and electronic tolling using video analytics, computer vision, edge networking, and deep learning algorithms.

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Use Case

Manufacturing Productivity Optimization 

Use video, analytics, and AI to monitor factory production, identify constraints, enhance worker safety, and improve overall factory production and efficiency.

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Use Case

Network Video Recorder

Integrate video streams from a variety of edge devices into computer vision applications.

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Use Case

Industrial Cloud to Edge AI

Leverage Microsoft Azure* Machine Learning to continuously retrain and improve accuracy or increase capabilities of edge inferencing models.

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Use Case

Operational Efficiency and Safety 

Increase supply efficiency and worker safety in industrial environments.

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Use Case

Thermal Anomaly Detection

Overheating is a common manufacturing anomaly. Use AI and time-series data to detect a departure from a reference thermal value or long-term average.

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Use Case

Store Traffic Analytics

Gain insight into consumer behavior by using computer vision models to identify patterns in customer volume and flow.

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Edge Insights for Vision

Deploy and optimize computer vision and deep learning workloads. Run multiple inference workloads on a single chip. Implement as a containerized architecture or stand-alone runtime.

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Documentation

Edge Insights for Industrial

Integrate video and time-series data analytics on edge compute nodes. Run concurrent workloads on ready-to-use containerized analytics pipelines. Support acceleration and distribution of video analytics on CPUs, GPUs, and VPUs.

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Documentation

Converged Edge Insights

Build edge applications for next-generation networks, including 5G. This package makes it easy to configure the OpenNESS toolkit for on-premises or network edge deployments. Create applications that run on your choice of cloud framework on networks managed by any service provider.

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Documentation

Product and Performance Information

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