Vision AI solutions with Azure IoT Edge

This series of articles describes how to plan and design a computer vision workload that uses Azure IoT Edge. You can run Azure IoT Edge on devices, and integrate with Azure Machine Learning, Azure Storage, Azure App Services, and Power BI for end-to-end vision AI solutions.

Visually inspecting products, resources, and environments is critical for many endeavors. Human visual inspection and analytics are subject to inefficiency and inaccuracy. Enterprises now use deep learning artificial neural networks called convolutional neural networks (CNNs) to emulate human vision. Using CNNs for automated image input and analysis is commonly called computer vision or vision AI.

Technologies like containerization support portability, which allows migrating vision AI models to the network edge. You can train vision inference models in the cloud, containerize the models, and use them to create custom modules for Azure IoT Edge runtime-enabled devices. Deploying vision AI solutions at the edge yields performance and cost benefits.

Use cases

Use cases for vision AI span manufacturing, retail, healthcare, and the public sector. Typical vision AI use cases include quality assurance, safety, and security.

Quality assurance

In manufacturing environments, vision AI can inspect parts and processes fast and accurately. Automated quality inspection can:

  • Monitor manufacturing process consistency.
  • Check proper product assembly.
  • Provide early defect notifications.

For an example scenario for this use case, see User scenario 1: Quality control.

Safety and security

Automated visual monitoring can scan for potential safety and security issues. Automation can provide more time to respond to incidents, and more opportunities to reduce risk. Automated safety monitoring can:

  • Track compliance with personal protective equipment guidelines.
  • Monitor and alert on entry into unauthorized zones.
  • Alert on unidentified objects.
  • Record unreported close calls or pedestrian-equipment near-misses.

For an example scenario for this use case, see User scenario 2: Safety.

Architecture

Vision AI solutions for IoT Edge involve several components and processes. The articles in this series provide in-depth planning and design guidance for each area.

Diagram that shows the basic components of an IoT Edge vision AI solution.

  1. Cameras capture the image data for input into the IoT Edge vision AI system. See Camera selection for Azure IoT Edge vision AI.
  2. Hardware acceleration on IoT Edge devices provides the necessary processing power for computer graphics and AI algorithms. See Hardware acceleration in Azure IoT Edge vision AI.
  3. ML models deployed as IoT Edge modules score the incoming image data. See Machine learning in Azure IoT Edge vision AI.
  4. Image scores that need attention trigger automatic alerts. See Alert persistence in Azure IoT Edge vision AI.
  5. The IoT Edge device sends relevant image data and metadata to the cloud for storage. Stored data is used for ML retraining, troubleshooting, and analytics. See Image storage and management for Azure IoT Edge vision AI.
  6. Users interact with the system through user interfaces like apps, visualizations, and dashboards. See User interfaces and scenarios in Azure IoT Edge vision AI.

Considerations

Reasons to migrate computer vision workloads from the cloud to the edge include performance and cost.

Performance considerations

  • Exporting less data to the cloud relieves strain on network infrastructure that can cause performance issues.
  • Scoring data locally helps prevent unacceptable response latency.
  • Local alerting avoids delay and added complexity.

For example, a person entering an unauthorized area might need immediate intervention. Positioning the scoring model near the data ingestion point allows near real-time image scoring and alerting.

Cost considerations

Scoring data locally and sending only relevant data to the cloud can improve the return on investment (ROI) of a computer vision initiative. IoT Edge custom vision modules can score image data per ML models, and send only images deemed relevant with reasonable confidence to the cloud for further processing. Sending only selected images reduces the amount of data going to the cloud and lowers costs.

Contributors

This article is maintained by Microsoft. It was originally written by the following contributors.

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Next steps

To continue with this series about IoT Edge vision AI, go on to the next article:

To learn more about CNNs, vision AI, Azure Machine Learning, and Azure IoT Edge, see the following documentation:

For more computer vision architectures, examples, and ideas that use Azure IoT, see the following articles: